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SIMPLE PULSE BLANKING TECHNIQUE and IMPLEMENTATION in DIGITAL RADIOMETER A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Noppasin Niamsuwan, B.Eng. ***** The Ohio State University 2005 Master’s Examination Committee: Prof. Joel T. Johnson, Adviser Prof. Ronald M. Reano Approved by Adviser Department of Electrical and Computer Engineering

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SIMPLE PULSE BLANKING TECHNIQUE and

IMPLEMENTATION in DIGITAL RADIOMETER

A Thesis

Presented in Partial Fulfillment of the Requirements for

the Degree Master of Science in the

Graduate School of The Ohio State University

By

Noppasin Niamsuwan, B.Eng.

* * * * *

The Ohio State University

2005

Master’s Examination Committee:

Prof. Joel T. Johnson, Adviser

Prof. Ronald M. Reano

Approved by

Adviser

Department of Electricaland Computer Engineering

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ABSTRACT

Radio Frequency Interference (RFI) can adversely impact microwave passive re-

mote sensing measurements, and prohibits passive observations outside protected por-

tions of the radio frequency spectrum. A microwave radiometer including a digital

back-end with a simple real-time RFI mitigation technique for reducing pulsed RFI

has been developed to address this issue; the process is termed “asynchronous pulse

blanking” (APB). The idea of this technique is to remove incoming data whose power

exceeds the mean power by a specified number of standard deviations. Although suc-

cessful performance of this algorithm has been qualitatively demonstrated through

local experiments with the digital radiometer, a detailed quantitative assessment of

its performance in a variety of RFI environments has not been reported.

To address this issue, a simulation study of the APB algorithm was initiated using

data obtained from the L-Band Interference Surveyor/Analyzer (LISA), an airborne

instrument developed for observing the RFI in the region 1200-1700 MHz. LISA was

deployed on NASA’s P3-B aircraft to observe RFI in flights in the US and Japan.

This data set is very useful for assessing the APB algorithm, since many RFI envi-

ronments were observed that include multiple sources of interference. Several aspects

of algorithm use and performance have been studied, including means for choosing

the algorithm’s parameters and the robustness of the method in a realistic RFI envi-

ronment. Effects of the blanking process on the final output are also examined.

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In order to further quantitatively demonstrate the performance of the system, a se-

ries of L-band sky observations was performed. These experiments were based on the

predictable brightness of the cold sky background, so that long-term measurements

of instrument performance are possible. The results reported in this thesis confirm

stability of the system for six hours of observation. In addition, post-processing tech-

niques proposed for removing other types of RFI have been tested.

While the algorithm is conceptually simple, developing a process that is both

automatic and robust requires careful consideration of the implementation details.

Issues regarding implementation in field-programmable gate array (FPGA) devices

are also discussed in this thesis.

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To my Mom and Dad,

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ACKNOWLEDGMENTS

Grateful acknowledgement is made to my adviser Prof. Joel T. Johnson, for his

genuine support and beneficial suggestions throughout this research. It is his efforts

and supports that make this research possible.

This work was supported by NASA Instrument Incubator Program project NAS5-

02001.

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VITA

January 17, 1981 . . . . . . . . . . . . . . . . . . . . . . . . . . . Born - Bangkok, Thailand

September 1999 . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.Eng., Electrical and ElectronicEngineering, Asian University ofScience and Technology, Thailand

1999-present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Graduate Research Associate,The Ohio State University.

PUBLICATIONS

Research Publications

N. Niamsuwan and J. T. Johnson, “Sky Observations at L-band with an InterferenceSuppressing Microwave Radiometer”. IEEE IGARSS 2005 International ConferenceProceeding, Jul 2005.

N. Niamsuwan, J. T. Johnson, and S. .W. Ellingson, “Examination of a simple pulse-blanking technique for radio frequency interference mitigation”. Radio Science, Vol.40, No. 3, Jun 2005.

N. Niamsuwan, J. T. Johnson, and S. .W. Ellingson, “Examination of a simple pulse-blanking technique for radio frequency interference mitigation”. Workshop in Miti-gation of Radio Frequency Interference in Radio Astronomy, conference proceedings,Jul 2004.

J. T. Johnson, N. Niamsuwan, and S. .W. Ellingson, “Digital Receiver for InterferenceSuppression in Microwave Radiometry”. NASA’s ESTO Conference Proceeding, Jun2005.

J. T. Johnson, A. J. Gasiewski, B. Guner, G. A. Hampson, S. W. Ellingson, R.Krishnamachari, N. Niamsuwan, E. McIntyre, M. Klein, and V. Leuski, “Airborne

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radio frequency interference studies at C-band using a digital receiver”. IEEE Trans.Geosc. Remote Sens., preprint

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FIELDS OF STUDY

Major Field: Electrical and Computer Engineering

Studies in:

Remote Sensing Prof. Joel T. JohnsonRandom Media and Rough Surface Scattering Prof. Joel T. Johnson

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TABLE OF CONTENTS

Page

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Chapters:

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Digital Radiometer Design . . . . . . . . . . . . . . . . . . . . . . . 2

2. Examination of Asynchronous Pulse Blanking Technique . . . . . . . . . 7

2.1 Principle of asynchronous pulse blanking . . . . . . . . . . . . . . . 72.2 Hardware Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 L-band Interference Surveyor/Analyzer (LISA) . . . . . . . . . . . 19

2.3.1 The Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.3.2 Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4 Simulation and Performance Evaluation . . . . . . . . . . . . . . . 252.4.1 Simulating the hardware process . . . . . . . . . . . . . . . 252.4.2 Choosing β2, Nblank, and Nwait . . . . . . . . . . . . . . . . 262.4.3 Output χ2 test . . . . . . . . . . . . . . . . . . . . . . . . . 322.4.4 Effect of blanking on integrated spectra . . . . . . . . . . . 362.4.5 Frequency domain blanking . . . . . . . . . . . . . . . . . . 41

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2.4.6 χ2-filter: alternative pulse blanking technique . . . . . . . . 422.4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3. Sky observation with digital radiometer . . . . . . . . . . . . . . . . . . . 44

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 443.3 Noise diode calibration . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.3.1 Experimental setup for noise-diode calibration . . . . . . . . 473.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503.6 Post-processing mitigation technique . . . . . . . . . . . . . . . . . 51

3.6.1 Cross-time blanking . . . . . . . . . . . . . . . . . . . . . . 543.6.2 Cross-frequency blanking . . . . . . . . . . . . . . . . . . . 553.6.3 Temperature control and stability of the system . . . . . . . 593.6.4 Narrow-band interferences at 1400 and 1403 MHz . . . . . . 603.6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4. Hardware issues for implementation in FPGA Components . . . . . . . . 65

4.1 Clock management for LISR3’s FPGA . . . . . . . . . . . . . . . . 674.2 Floorplan and Signal-Routing . . . . . . . . . . . . . . . . . . . . . 704.3 Implementation of slow scaling process . . . . . . . . . . . . . . . . 784.4 Thermal Control for Analog Down-converter . . . . . . . . . . . . . 79

4.4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Appendices:

A. LISA’s frequency channels . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

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LIST OF TABLES

Table Page

3.1 Contribution of narrow-band RFI to the average noise power (orderedby contributed error) . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

A.1 Frequency range of LISA’s channel 1 to 14 . . . . . . . . . . . . . . . 91

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LIST OF FIGURES

Figure Page

1.1 Block diagram of radiometer . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Simulated sampled signal. The blanker is triggered whenever the powerof the input data exceeds the threshold. It is possible that anotherpulse may be received while the blanker is occupied. Extra blankersare required to ensure that the whole pulse can be completely removed. 8

2.2 FIFO buffer and Nwait to control pre-pulse blanking region. . . . . . . 11

2.3 Definition of Nblank and Nwait: note time is incresing from left to right 12

2.4 Simulated case for too small Nblank. For Nsep clock cycles after eachblanking process is initiated, new trigger would not be recognized.Small blanking region would fail to suppress successive pulses receivedduring Nsep period. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5 Though Nblank is sufficiency large to blank all samples during Nsep

period, Eq. 2.6 can not guarantee that successive pulse occurs withinNsep period can be completely suppressed (circled area). . . . . . . . 18

2.6 Block diagram of an L-band Interference Surveyor/Analyzer (LISA) . 20

2.7 Map of January 3rd transit fight from Wallops Island, VA to Monterey,CA, including known locations of ARSR systems (marked with ’×’).Edge markings are latitude and longitude. . . . . . . . . . . . . . . . 23

2.8 Pulse interference at 1340 MHz during transit flight over Illinois. . . . 23

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2.9 Navigation path of LISA flight. The location of the aircraft (lat. 39.12◦

N, long. 90.87◦ W), marked with dot and circle, where interference inFigure 2.8 is received. The nearest ARSR site (lat. 38.70◦N, long.90.39◦ W) is highlighted. . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.10 Estimated power (solid line) transmitted from the ARSR site in Fig-ure 2.9. Free-space model has been used assuming all power is receivedfrom the main-lobe of the radar’s antenna. The measured receivedpower is also included (dotted line). . . . . . . . . . . . . . . . . . . . 24

2.11 Initialize stage of APB process . . . . . . . . . . . . . . . . . . . . . . 26

2.12 Initialization process for the simulation software. The process is slightlydifferent than that implemented in the actual hardware due to theamount of data that is available. Several jumps in the first stage (forceupdate) corresponds to the presence of RFI. After 20480 samples (5captures), the mean and variance converge. The second stage (blankeron) of the process repeats the mean/variance computation with theblanker turned on to reduce the effect of pulsed RFI. . . . . . . . . . 27

2.13 Percentage of samples exceeding threshold for given β2. All 145 cap-tures of LISA’s channel 7 data are used. . . . . . . . . . . . . . . . . 29

2.14 Detection Threshold (for detecting pulses) and Reference Threshold(for estimating number of pulses). Nwait=0, Nblank=1536 are used inthe APB output results shown . . . . . . . . . . . . . . . . . . . . . . 30

2.15 (Pin−Pout)/Pin×100%, where Pin and Pout are the number of samplesexceeding the “reference threshold” at input and output of the APBalgorithm, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.16 Percentage of blanked samples ((Pin − Pout)/Pin × 100%) of LISA’schannel 13 data. The pulses shorter than 100 ns were ignored in thiscomputation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.17 Percentage of blanked samples of LISA’s channels 7–12. . . . . . . . . 33

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2.18 Top: Noise power from 5 captures sucessively taken in the vicinityof Wheeling, IL (approx. 39.87◦ N lat, 80.67◦ W long) at 22,000 ft.LISA is tuned to channel 7 (1324 - 1344 MHz). Several examples ofpulsed RFI are observed. Bottom: χ2 of APB output as Nblank is varied(β2=40, Nwait=0). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.19 Definition of BLANK, PARTIAL BLANK and NO BLANK frames . . . . . . 37

2.20 Frequency spectrum of an example PARTIAL BLANK frame, comparedto the entire NO BLANK average for the corresponding capture . . . . . 39

2.21 Impact of PARTIAL BLANK on the final output. Top: Method 2: Instan-taneous Scaled averages compared to method 1. Bottom: The error ofmethods 2 (inst. scaled) and 3 (slow scaled) . . . . . . . . . . . . . . 40

2.22 Mean error in total power estimate from methods 2 and 3 in LISAchannels 7-13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.23 Frequency-domain APB algorithm results. . . . . . . . . . . . . . . . 42

3.1 Top: 10ft-diameter parabolic reflector. Bottom: Schematic diagram ofantenna front-end (AFE) . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.2 Experimental Setup for noise-diode calibration . . . . . . . . . . . . . 48

3.3 Estimated ENR vs Lx pad . . . . . . . . . . . . . . . . . . . . . . . . 49

3.4 APB-OFF: Top–Spectrogram, color scale represents sky brightness tem-perature in Kelvin. Bottom–Average brightness temperature VS Time.Without RFI mitigation some of data are corrupted. Pulsed RFI at1330 MHz is expected and responsible for 3-Kelvin oscillation in theaverage temperature plot. . . . . . . . . . . . . . . . . . . . . . . . . 52

3.5 APB-ON: Spectrogram and average noise temperature plot observedwith the blanker turned on. . . . . . . . . . . . . . . . . . . . . . . . 53

3.6 APB-ON with cross-time blanking applied . . . . . . . . . . . . . . . 54

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3.7 Xfreq-I cross-frequency RFI detection algorithm. A simple thresholdis derived from the average noise temperature over the 100 MHz spec-trum. Th possible lowest threshold (dashed line) is limited by the gainpattern of the instrument. . . . . . . . . . . . . . . . . . . . . . . . . 56

3.8 Xfreq-II cross-frquency RFI detection algorithm. The threshold isderived from the derivative of the data. This allows weak RFI to bedetected. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.9 Xfreq-III cross-frequency RFI detection algorithm. Gain pattern isfirst estimated by interpolating local minima of the frequency spectrum(Left panel). This allows the contribution of uncalibrated instrumentgain pattern to be considered in the detection process. . . . . . . . . 58

3.10 APB-ON with cross-time and cross-frequency blanking applied. . . . 59

3.11 A survey of 1400 MHz RFI. The antenna was focused to different di-rections. Strong RFI is found from the East while there was no otherRFI received from other directions. . . . . . . . . . . . . . . . . . . . 61

3.12 Multi-window plot show the spectrum centered around 700 MHz and1400 MHz measure at the same time. The antenna is directed into theradiometer back-end rack. . . . . . . . . . . . . . . . . . . . . . . . . 62

3.13 1403 MHz radiation from spectrum analyzer. Observation at 1403MHz with a spectrum analyzer also found the RFI leaked from theinstrument itself . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.1 LISR2 digital backend; the vertical cascade of three circuit boards nearthe left hand side contains the dual ADC sections (upper and lowerboards) and the digital channel combination and filtering (DIF) sec-tion (center board). The APB section for removing temporal pulses isalso implemented on the center board. Following the vertical cascadeto the right is the FFT processor, then the SDP section for power com-putation and integration operations. Finally a “capture card” providesthe interface to the PC. Microcontrollers are also included on each card(the smaller attached circuit boards with ethernet cables) to enable PCsetting of FPGA parameters through an ethernet interface. . . . . . . 66

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4.2 LISR3 digital backend; underneath a heat-sink on the center board isthe Stratix FPGA which contains the entire processor. The boardson the left and right of the Stratix board are the ADC cards. Thesystem clock used to run the Stratix processor is also derived fromone of these ADC cards. Above the Stratix card is a “capture card”providing a 256K-FIFO buffer and the interface to the PC. Only asingle Microcontroller card (the smaller card attached to the bottomof the Stratix board) is needed in this prototype. Except for the Stratixcard, the other parts of the hardware are the same as in LISR1 andLISR2. The clock-generator and power-supply (top-left and bottom-right respectively) are also the same as in previous prototype. . . . . 68

4.3 Clock and data signal at the output of ADC cards (dashed line) beforecrossing the SCSI-3 connector to the Stratix card (solid line). The min-imum high (1.7 V) and maximum low (0.7 V) voltage level accordingto LVTTL/LVCMOS requirement are also shown as a reference. Thelower plots demonstrate the expected logic level to be recognized bythe Stratix FPGA according to these reference levels. . . . . . . . . . 69

4.4 Dual-clock FIFO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.5 Clock regeneration and synchronization scheme for Stratix FPGA . . 71

4.6 Final layout of Stratix FPGA. Yellow-shaded area shows occupied re-sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.7 Floorplan of the Stratix FPGA. All marked/labeled areas are assignedas Logic-Lock region. During “fitting” process, the simulator will at-tempt to allocate resources according to these given criteria. The con-nections to the rest of the system are also shown. . . . . . . . . . . . 73

4.8 DSPs and M4K-RAM locations in Stratix FPGA. The location of PLLused to generate the “master clock” for the entire chip is also shown. 75

4.9 M512-RAM and M-RAM locations in Stratix FPGA . . . . . . . . . 76

4.10 Detailed block diagram of APB and SDP. . . . . . . . . . . . . . . . . 78

4.11 Slow-scaling test results . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.12 Temperature of down-converter compared to the ambient temperature. 80

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4.13 Power at distict ports of the front-end switch: shown are Noise Diode,Terminator and Antenna (H-Pol). The front-end temperature is set to45 ◦C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.14 Comparison of the power seen at antenna port and the tempearutureof down-converter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.15 Thermal insulation plan and location of the temperature probes . . . 84

4.16 Temperature of down-converter enclosure for sky-observation in Chap-ter 3. The temperature measured from “Control” probe is used as aninput for controlling feed-back loop. . . . . . . . . . . . . . . . . . . . 86

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CHAPTER 1

Introduction

Radio Frequency Interference (RFI) mitigation is very important for passive re-

mote sensing observation. At L-band, since only a 27 MHz spectral window (1400–

1427 MHz) is preserved for passive operations, the presence of numerous RFI sources,

including strong pulsed RFI from ground-based aviation radars (GBARs) [1, 2], cause

spectroscopy outside this protected band can be adversely impacted. To address this

issue, a digital radiometer with a capability of detecting and rejecting pulse-like RFI

was developed at the Ohio State University ElectroScience Laboratory. This RFI

mitigation technique relies on the high temporal sample rate of the system. For ra-

diometers operating at a sufficiently high sample rate, a simple strategy for reducing

pulsed RFI is to remove incoming data whose power exceeds the mean power by a

specified number of standard deviations. It may also be advantageous to remove data

within a specified time region before and after this “trigger” data, to ensure that any

pre- and post-detection “pulse” information is successfully removed. The process is

termed “asynchronous pulse blanking” (APB) because no periodic properties of the

interference source are assumed.

1

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Successful performance of this algorithm has been qualitatively demonstrated

through local experiments with the digital radiometer [3]. However, a detailed quan-

titative assessment of its performance in a variety of RFI environments has not been

reported.

This thesis involves an examination of APB technique. The next section serves as

a preview of the digital radiometer’s design. Detailed discussion on this mitigation

technique can be found in Chapter 2 to 4.

1.1 Digital Radiometer Design

Under the support of the NASA “Instrument Incubator Program” (IIP), devel-

opment of a digital-receiver based “L-band Interference Suppressing Radiometer”

(LISR) was initiated in December 2001. The project is primarily focused in L-band

and C-band given the received strong RFI sources at L-band and limited protected

spectrum, as well as the absence of protected spectrum at C-band. The project is

focused on passive microwave observation of the Earth in these bands; however, the

radiometer can be applied for L-band radio astronomy, and both applications are

severely impacted by RFI.

A block diagram of the L-band radiometer is shown in Figure 1.1. The analog

front end downconverts an 80 MHz swath of spectrum from L-band to 150 MHz, and

samples this signal at 200 MSPS using 10 bits. Note the system can also be operated

at frequencies other than L-band simply by modifying the analog low-noise front-end

and downconverter sections. Using a large number of ADC bits opens the possibility

of reduced gain in the radiometer frontend and downconverter sections, as higher

dynamic range is typically achieved in ADC’s by increasing sensitivity (i.e. lowering

2

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L o w � n o i s eF r o n t � e n d A D C2 � c h a n n e l o f8 0 M H z B W@ 1 5 0 M H z D i r e c t ( C o n v e r s i o n R e c e i v e r

A n a l o gD o w n C o n v e r t e rD i g i t a l I F( D I F )

A s y n c h r o n o u sP u l s e B l a n k e r( A P B )1 K F F TI n t e g r a t i o n ( S D P )C a p t u r e b o a r d( 2 5 6 K F I F O )

A D C D i g i t a l I F( D I F )2 0 0 M S P S @ 1 0 b i t s 5 0 M H z B W @ + 2 5 M H z , 1 0 0 M S P S 1 6 + 1 6 b i t s

5 0 M H z B W @ d 2 5 M H z , 1 0 0 M S P S 1 6 + 1 6 b i t s

Figure 1.1: Block diagram of radiometer

the power required to toggle the lowest ADC bit.) Reduced gain is desirable in general

in order to improve receiver stability, and thereby potentially reduce thermal control

requirements throughout the entire system. Trade-off studies of receiver stability

versus gain were not performed under the IIP project however.

Although resolving 100 MHz with one such ADC would be possible, it was deemed

preferable to utilize 2 ADC’s sampling 50 MHz each so that digital filtering could be

incorporated into the RFI processor to limit receiver bandwidth digitally. Use of

3

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digital filters is desirable due to possible stability issues of analog filters with temper-

ature and other environmental variations, particularly near the cutoff frequencies of

the filter response. The design developed keeps the analog filter passband wider than

that ultimately set by the digital filters, so that the near cutoff regions of the analog

filters do not contribute significantly to the observed bandwidth.

Accordingly, the 100 MHz bandwidth is split into 2 50 MHz channels, and each

channel is then sampled at 200 MSPS using 10 bits. The resulting digital data of

each channel is centered at 50 MHz and is spectrally reversed due to the use of the

second ADC Nyquist zone. The “Digital IF” (DIF) FPGA module downconverts each

channel to 0 Hz (so now the samples are complex-valued), digitally filters each to 50

MHz bandwidth, decimates by 2, and then up- or down-converts the two channels

to center frequencies of +/-25 MHz (still complex). Finally both channels are added

so that -50 to 50 MHz data emerges from the DIF module in 16-bit “I”+16-bit “Q”

format at 100 MSPS.

Following the DIF output is a cascade of FPGA modules which can be pro-

grammed to perform a variety of functions. The favored strategy currently is as

shown in Figure 1.1: mitigation of radar pulses using APB, channelization into 100-

kHz bins using a 1K FFT, and integration to generate power spectra.

The APB is designed to detect and blank radar pulses, which typically are the

dominant source of external L-Band RFI below 1400 MHz. Radar pulses range from

2-400 µs in length and occur 1-75 ms apart [1]. To detect these pulses, the APB

maintains a running estimate of the mean and variance of the sample magnitudes.

Whenever a sample magnitude greater than a threshold number of standard devia-

tions from the mean is detected, the APB blanks (sets to zero) a block of samples

4

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beginning from a predetermined period before the triggering sample, through and

hopefully including any multi-path components associated with the detected pulses.

APB operating parameters are adjustable and can be set by the user.

Following the APB is a length-1K complex FFT, which achieves approximately

98% duty cycle in performing the FFT computations (i.e., 98% of the data is FFTed,

and the rest is lost). A triangular window is applied before the FFT. Planned but

not yet implemented is a frequency-domain blanking (cross-time blanking as in Sec-

tion 3.6.1) module, which is similar in concept to the APB, except applied indepen-

dently to each frequency bin. The purpose of this module will be to exploit the

processing gain achieved through channelization to detect and excise weak, relatively

narrowband RFI. The FFT output is processed through a “spectral domain proces-

sor” (SDP) module which computes magnitude-squared for each frequency bin and

computes a linear power average over many FFT outputs. These results are passed

at a relatively low rate to a PC via a capture board. Total power can be computed

by summation of frequency bins within the digital hardware, or the same process can

be implemented within the PC for increased flexibility in monitoring RFI, selecting

subbands, and so on.

This radiometer has been used in both radio astronomy and remote sensing exper-

iments locally at the ElectroScience Laboratory [3] and at the Arecibo observatory

in Puerto Rico [2, 4]. The results qualitatively show the APB approach to be suc-

cessful for pulsed RFI mitigation, although a detailed performance assessment will be

discussed in this thesis.

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In Chapter 2, a simulation study of the APB algorithm is discussed. The effects of

each controlling parameter and optimization of the overall process has been investi-

gated. Performance of the blanking algorithm is evaluated and presented in the same

chapter. Chapter 3 describes a deployment of the system for sky observation. This is

of interest in order to provide quantitative assessment of the system performance. A

discussion and simulation on post-process data enhancement including algorithms to

detect non-pulsed RFI, e.g. narrow-band RFI, are also reported. Finally, hardware

issues regarding implementation on field-programmable gate array (FPGA) devices

are summarized in Chapter 4.

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CHAPTER 2

Examination of Asynchronous Pulse Blanking Technique

2.1 Principle of asynchronous pulse blanking

The APB algorithm is intentionally simple to keep implementation in hardware

feasible. As illustrated in Figure 2.1, a pulse-detection criterion is to declare RFI

detection whenever any high-power signal samples are observed. For each sample x in

the data stream, ||x||2 (the squared-magnitude of sample, i.e. the power) is computed

and compared to the threshold. This threshold is defined as βσ + m where σ and m

are the standard deviation and the mean, respectively. Both mean (m) and standard

deviation (σ) are estimated from previous data samples. The β is a parameter to

determine “aggressiveness” of detection process. Large β value may allow small RFI

to pass through while small β value will ensure that all RFI will be detected but

it might trigger some large noise peaks and treat them as RFI. Consequently, the β

parameter should be optimized to trade-off loss of noise power integration time versus

blanking performance. Section 2.4.2 discusses how to choose a proper value for this

parameter.

From a hardware perspective, computing the standard deviation is an expen-

sive process both in terms of implementation complexity and processing time. The

7

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0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0T i m e ( � s e c )

P ower(li nearuni t s)T h r e s h o l d

N B L A N K

N S E P

N W A I TP u l s e R F I d e t e c t e d

B l a n k i n g R e g i o n

P u l s e R F I d e t e c t e d

T i m e I n d e xFigure 2.1: Simulated sampled signal. The blanker is triggered whenever the powerof the input data exceeds the threshold. It is possible that another pulse may bereceived while the blanker is occupied. Extra blankers are required to ensure that thewhole pulse can be completely removed.

8

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variance (σ2) is rather preferable to deal with. Therefore, the detection criteria is

rearranged in equivalent form as:

(||x||2 −m)2 ≥ β2σ2 (2.1)

Because some portion of the pulse typically appears in the data stream before its

power is high enough to trigger the threshold (Figure 2.1), it is required in the pulse

removing process also to remove samples prior to a detection. To blank pre-detection

samples, a buffer (memory) is required in the system. The detection process is then

performed on the sample entering the buffer while the blanking operations and other

successive operations are performed on the samples exiting the buffer. A simple

first-in-first-out (FIFO) buffer suffices; and the length of the FIFO (FIFO LENGTH)

determines maximum number of samples that can be blanked before the detected

sample.

Even though a FIFO buffer allows all the samples in the buffer to be blanked if

the pulse is detected, this is not always necessary. Blanking the whole buffer may

removed desired noise information and should be avoided if possible. To control the

length of the pre-pulse blanking, the parameter Nwait is introduced. Once detection

is asserted, a process is initiated to wait for Nwait samples before the FIFO output

is set to zero. The number of pre-detection samples remaining in the buffer after

Nwait clock cycles have elapsed is then FIFO LENGTH - Nwait. The FIFO output is

then continually forced to zero for a specified number of clock cycles. The length of

this blanking “window” is determined by the final APB parameter, Nblank. The set

of parameters β, Nwait, Nblank, and FIFO LENGTH are the basic conceptual quantities

that control the APB process.

9

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Figure 2.2 demonstrates the blanking process with the use of a FIFO. The pulse

is detected (at the FIFO input) at time t2. On the left panel, Nblank was set to 4

clock cycles; Nwait is not utilized (Nwait=0). The blank signal is therefore asserted

immediately after detection. As a result, 4 samples, starting from the one appearing

at the FIFO output at the time of detection, will be removed. In contrast, with

Nwait = 2 (the right panel), the blank signal has been delayed for 2 completed clock

cycles leaves only one pre-pulse sample is set to zero. Nblank of 2 is sufficient to

completely remove pulse contribution in this case.

Figure 2.3 illustrates simulated incoming signal power. A simulated pulse is also

shown in this figure. It can be clearly seen from the figure how the blanking region

is determined for given parameters. The combination FIFO LENGTH-Nwait controls

the size of the blanking region prior to detection. This value should be sufficiently

large to ensure that even largest pulses can be completely removed. The size of the

total blanking region is controlled by the parameter Nblank which obviously must be

large enough to ensure that any possible multi-path contribution of the pulses can be

successfully blanked.

2.2 Hardware Issues

Since the technique is intended to be implemented using FPGA, the algorithm

needs to be designed to exist within limited hardware resources.

FIFO LENGTH

Ideally, the FIFO should be made as large as possible in order to allow the largest

pre-detection period. However, hardware issues (i.e. size of the FPGA used) limit

the maximum FIFO size which can be included. For the IIP radiometer and for

10

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P u l s e D e t e c t e dB l a n k t 0 t 1 t 2 t 3 t 4 t 5 t 6 t 7 t 0 t 1 t 2 t 3 t 4 t 5 t 6 t 7I n p u t F I F O O u t p u tP u l s e D e t e c t e d

B l a n k O u t p u t( 1 )B l a n k O u t p u t( 2 )B l a n k O u t p u t( 3 )B l a n k O u t p u t( 4 )

P u l s e D e t e c t e dW a i t( 1 )W a i t( 2 )

B l a n k O u t p u t( 1 )B l a n k O u t p u t( 2 )

P u l s e D e t e c t e dB l a n kS t r o n g p u l s et r i g g e r s t h e d e t e c t o r

t i m e t i m e

Figure 2.2: FIFO buffer and Nwait to control pre-pulse blanking region.

11

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0 5 10 15 200

10

20

30

40

Pow

er

(dB

)

Sample Index (× 1000)

InputOutput

FIFO_LENGTH - Nwait

Nblank

Pulse Detected

Figure 2.3: Definition of Nblank and Nwait: note time is incresing from left to right

the remainder of the simulations performed in this thesis, FIFO LENGTH was set to

1 K due to size limitations of the FPGA components used in the prototype system.

This length of FIFO (1 K – representing 10.24 µsec of data given the 10 nsec sample

spacing) is shown to be sufficient for application at L-band (Section 2.4)

Running Filter

The APB algorithm requires a running estimate of the mean (mt) and variance

(σ2t ) of the incoming signal power. These estimates are computed as:

mt = µmean ×mt−1 + (1− µmean)× ||x||2 (2.2)

σ2t = µvar × σ2

t−1 + (1− µvar)× (||x||2 −mt)2 (2.3)

where mt−1 and σ2t−1 are the mean and variance generated on the previous clock

cycle, and µmean/var ≈ 1 are constants that control the response rate of these simple

averaging filters. In the APB hardware, the above computations are accomplished

by groups of fixed-point multipliers, adders, and delay registers. In addition, a state

machine controls these filters so that contributions from incoming post-detection data

12

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that will be blanked are not included in computing mt and σ2t . The quantities (||x||2−

mt)2 and σ2

t available from equation (2.3) additionally make it convenient to perform

the pulse detection test as (||x||2 −mt)2 > β2σ2

t , because the square root operation

needed in computing σt from σ2t is then avoided. Further discussions of the threshold

will thus focus on β2 as opposed to β due to this fact.

Large Bit-width

Due to the squaring operation in computing ||x||2 (represented originally as 16 bit

I and 16 bit Q), a wide datapath of 32 bits is required to preserve accuracy for this

quantity. The estimate of σ2 further computes the square of ||x||2, requiring 64 bits.

The high dynamic range of the filter constants µ ≈ 1 and (1−µ) ≈ 0 further increases

the number of bits required in estimating the running mean and variance of ||x||2.

Use of 12 bits (µ = 0.999756) to capture both µ and (1 − µ) was found desirable

in setting an appropriate averaging length for the filter. The resulting arithmetic

operations grow to a maximum size of 76 bits, and severely limit the computation

speed of the detector due to this large size.

Processor Speed

Because of the large bit growth of the APB processor and speed limitations of the

FPGA components currently used, the detection operation can not be performed on

100% of the incoming data. The current implementation of the design can process

only one in four samples (25%) of the data for detection. A decimation controller

is introduced to sub-sample the input data stream to every 4 samples (40 nsec for

10 nsec sample spacing) for detection operations. However, since radar pulses are

typically longer than this sub-sampling period, they are still detectable even under this

13

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limitation. This sub-sampling operation refers only to samples analyzed for detection,

the remainder of the system, including the blanking operation, still operates on 100%

of the incoming data. Note an average of four successive samples could also be used

in the decimation controller rather than the sub-sampling method implemented, and

would likely result in improved detection performance. However for simplicity in

implementation the sub-sampling method was utilized.

Control of Blanking Process

When a detection is obtained, a simple state machine is initiated to wait for

Nwait clock cycles, then blank the output of the FIFO for Nblank clock cycles. The

state machine controlling this process is called a “blanking timing register” (BTR).

Figure 2.1 demonstrates a case that a second RFI pulse is received while the BTR is

occupied for blanking process of the recently detected pulse. Note the second pulse

exceeds the detection threshold only within the period of the first blanking operation,

and does not exceed the threshold after the first blanking operation has completed.

If no second BTR were initiated, a portion of the second pulse would be allowed to

remain in the data. However a second blanking operation is initiated and continually

removes second pulse contributions.

Although the BTR is simple to implement in hardware, it still occupies a non-

negligible portion of the available FPGA logic. Therefore it is not possible to have an

unlimited number of BTR’s in the system. Because a BTR is unavailable to begin a

blanking process for Nwait +Nblank cycles after it has been “triggered”, the possibility

of having no BTR’s available to blank a detected pulse must be considered if only a

finite number of BTR’s exist.

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A limitation in the number of BTR’s also raises another issue: a large pulsed

interferer presumably could trigger all available BTR’s on successive clock cycles, with

the resulting blanking windows overlapped almost completely at the FIFO output.

The ability of the APB to blank detected pulses would then be compromised for

approximately Nwait+Nblank clock cycles when the initial BTR would become available

again. This problem motivates the introduction of a parameter Nsep, defined to be

a number of clock cycles after a detection during which no further BTR’s will be

triggered. If M BTR’s are available, then setting

Nsep =1

M(Nwait + Nblank) (2.4)

will ensure that a BTR is always available for triggering once Nsep has elapsed. Al-

though Nsep values larger than the above value would also satisfy this requirement,

minimizing Nsep is desirable to ensure maximal pulse detection.

While introduction of Nsep manages the use of a finite number of BTR’s, it also

may compromise the ability of the APB to remove detected pulses, if a detection

occurs within Nsep clock cycles of an earlier detection. Although a new BTR process

will not be initiated for Nsep clock cycles, the APB algorithm can still be designed

so that all post-detection signal information within Nsep clock cycles will be blanked.

Referring to Figure 2.3, the post-detection blanking region contains Nblank + Nwait −

FIFO LENGTH samples for a fixed Nblank. Choosing

Nsep ≤ Nblank + Nwait − FIFO LENGTH (2.5)

will ensure that all signal information within Nsep samples after a detection will be

blanked. Figure 2.4 demonstrates a simulated case when this criterion is violated.

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P u l s e d e t e c t e dT h r e s h o l d

B l a n k i n g R e g i o n( B T R # 1 )

N s e pP o s t & d e t e c t i o n R e g i o n

Figure 2.4: Simulated case for too small Nblank. For Nsep clock cycles after eachblanking process is initiated, new trigger would not be recognized. Small blankingregion would fail to suppress successive pulses received during Nsep period.

16

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Combining equations (2.4) and (2.5) results in the condition

Nblank ≥M

M − 1FIFO LENGTH−Nwait (2.6)

to ensure that all samples exceeding the specified threshold will be blanked. Although

this equation appears to require that Nblank increase with FIFO LENGTH, the associated

Nwait would also likely increase with FIFO LENGTH so that Nblank would not necessarily

increase. The digital radiometer prototype at present contains 4 BTRs; this number

will be assumed in the simulations of Section 2.4. For this case with FIFO LENGTH =

1024, Nblank should be chosen ≥ 1366 − Nwait. Note that even though the choice

of equation (2.6) ensures that all samples exceeding the threshold will be blanked,

successive pulses occurring within the Nsep period of an initial pulse will still not have

a complete blanking window surrounding all their contributions (Figure 2.5).

17

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P u l s e d e t e c t e dT h r e s h o l d

B l a n k i n g R e g i o n ( B T R # 1 )

N s e p

Figure 2.5: Though Nblank is sufficiency large to blank all samples during Nsep period,Eq. 2.6 can not guarantee that successive pulse occurs within Nsep period can becompletely suppressed (circled area).

18

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2.3 L-band Interference Surveyor/Analyzer (LISA)

2.3.1 The Design

In order to perform a more complete assessment of the APB algorithm, it is

desirable to study performance in a complex and varying RFI environment. However,

experiments at L-band with the radiometer prototype to date have occurred only in

the local environment and at the Arecibo facility. Furthermore, it is desirable to

perform these assessments in a software rather than hardware environment, so that

quantitative information can be obtained as APB parameters are varied for a fixed

specific dataset. The dataset from the LISA sensor provides an excellent resource for

these studies.

LISA, completed in September 2002, was developed as a means to observe sources

of radio frequency interference (RFI) in the region of 1200-1700 MHz from an air-

borne platform [5, 6, 7]. LISA’s block diagram is shown in Figure 2.6. Physically,

LISA is comprised of a small antenna/ front-end unit (AFE) and an equipment rack.

The antenna unit consists of a nadir-facing cavity-backed planar spiral antenna with

an integrated custom RF front-end including filtering and calibration circuitry. The

antenna has a very broad pattern (approximately “cos θ”) and is reasonably well-

matched over the span of the observations reported here. The antenna unit is con-

nected to an equipment rack mounted in the aircraft cabin by a long and fairly lossy

coaxial cable. Although the cable loss degraded the sensitivity of the instrument,

the resulting gain profile was an important factor in preserving the linearity of the

system. While observing strong RFI waveforms, this consideration was paramount,

but comes at the expense of the system’s ability to detect weak RFI.

19

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F r o n t � e n dA D CA D C IQ

S p e c t r u m A n a l y z e r2 5 6 KF I F O

B P F : 1 2 0 0 ' 1 8 0 0 M H zL o n g C o a x2 0 M S P S

L P F : 7 M H zD i r e c t ' C o n v e r s i o n R e c e i v e r 1 2 0 0 ' 1 7 0 0 M H z

Figure 2.6: Block diagram of an L-band Interference Surveyor/Analyzer (LISA)

Inside the equipment rack, the signal is delivered to a custom-designed coherent

sampling subsystem. This subsystem uses a direct-conversion receiver to tune (under

PC control) anywhere between 1200 MHz and 1700 MHz. “I” and “Q” signals at

baseband are low-pass filtered with ∼ 7 MHz cutoff and sampled at 20 MSPS, yielding

a digitized bandwidth of ∼ 14 MHz. The output samples are queued in a 16K-sample-

long first-in-first-out (FIFO), whose contents are acquired by means of the PC parallel

port. These 16K “captures” represent an 819.2 µsec sample of the received field

at the current center frequency, and require approximately 1 sec to be transferred

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and recorded on the computer. During flight, the coherent sampling system was

successively tuned through center frequencies of 1250, 1264, ..., 1698 MHz; these

distinct bands are called channel 1, 2, etc. in what follows. Channels 1–14 and their

corresponding operating frequencies are tabulated in Table A.1, Appendix A, as a

reference.

For each channel, 5 819.2 µsec captures are performed before tuning to the next

center frequency. After completing a sweep of all channels, a backup operation was

performed to a separate storage component in the computer system. The time re-

quired for this operation caused a 10 to 15 minute delay between “sweeps”. The final

dataset provides a high temporal resolution but very low duty cycle observation of 4.1

msec captured per LISA channel per 12 to 15 minutes. This high resolution recorded

data allows the APB algorithm to be simulated and studied in softwares opposed to

hardware.

2.3.2 Deployment

For the datasets considered here, LISA was installed in NASA’s P-3 research

aircraft, which is based at the Wallops Flight Facility (WFF) located at Wallops Is-

land, VA. The LISA antenna unit was mounted in the tail radome. The loss due to

transmission through the radome is unknown and may be another factor degrading

sensitivity. The data used in the simulations to follow were collected during a single

flight on January 3, 2003 along an east-west track from Wallops Island, VA to Mon-

terey, CA at approximately 22,000 ft. These data obviously include observation in

a variety of RFI environments. Data from LISA channels 6-13 (1310 − 1428 MHz)

is used in the simulations; for each of these channels, 145 captures (5 captures × 29

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sweeps) were available. LISA was also deployed in the “Wakasa Bay” remote sensing

campaign in Japan through Feb of 2003; data on the transit flights from Monterey to

Japan were also collected [7]. However, the RFI environments in these flights show

less variation than that of the January 3rd flight, and so these data are not considered

further in this simulation.

Figure 2.7 illustrates the navigation path of P-3 aircraft on January 3rd. During

this transit flight, the aircraft was in proximity of several Air Route Surveillance Radar

(ARSR) radar sites. Several interferers were observed at center frequencies ranging

from 1313 to 1349 MHz, and in many cases can be correlated with known air traffic

control sites. Figure 2.8 shows a sample of an interferer with multipath contributions

detected close to one of ARSR sites in Illinois (Figure 2.9). The frequency of this

pulse is 1340 MHz with a pulse width of 6 µsec. The power seen at the antenna

terminal is plotted in Figure 2.10 along with estimated power according to a free-

space propagation model. The plot shows some feature even though the model seems

to overestimate the power level. Considering rotation period of the radar suggests

that the signal is possibly received from the side-lobe of the radar’s antenna; this

explains the difference between the estimated power level and observed data.

Other interferers, whose pulse widths range from 2–6 µs, were detected when the

aircraft was passing other radar sites as well. Some of those pulses were received

without significant multipath effects while some of them show multipath contribu-

tions. Generally, this campaign provides useful datasets for RFI mitigation testing

purposes.

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−120 −110 −100 −90 −80 −7035

40

Figure 2.7: Map of January 3rd transit fight from Wallops Island, VA to Monterey,CA, including known locations of ARSR systems (marked with ’×’). Edge markingsare latitude and longitude.

500 505 510 515 520 525 530−80

−60

−40

Pow

er (

dBm

)

Time(µs)

(a) Pulse length of approximately 6 µS is observed. The multipath contribution isalso received.

1325 1330 1335 1340

−100

−50

Frequency (MHz)

Pow

er (

dBm

)

(b) FFT reveals the frequency of the pulse is approximately 1340 MHz

Figure 2.8: Pulse interference at 1340 MHz during transit flight over Illinois.

23

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Figure 2.9: Navigation path of LISA flight. The location of the aircraft (lat. 39.12◦

N, long. 90.87◦ W), marked with dot and circle, where interference in Figure 2.8 isreceived. The nearest ARSR site (lat. 38.70◦N, long. 90.39◦ W) is highlighted.

16:39:41 16:39:44 16:51:25 16:51:26 17:03:24 17:15:36−70

−60

−50

−40

−30

Pow

er (

dBm

)

Time(HH:MM:SS)

EstimatedMeasured

Figure 2.10: Estimated power (solid line) transmitted from the ARSR site in Fig-ure 2.9. Free-space model has been used assuming all power is received from themain-lobe of the radar’s antenna. The measured received power is also included(dotted line).

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2.4 Simulation and Performance Evaluation

The APB algorithm is examined in various aspects. First, an optimum choice of

β2, Nblank and Nwait parameters is investigated. Next a χ2-test [8] is utilized in order

to verify the success of the algorithm by evaluating statistical properties of the data

before and after pulse-blanking. Finally, errors introduced from the pulse-blanking

stage will be explored.

2.4.1 Simulating the hardware process

This section highlights some concerns about simulation of the radiometer’s digital

processing units. With a few exceptions, all aspects of hardware limitations have been

taken into account in the simulating software. For instance, the number of BTR’s was

limited to 4, and the FIFO LENGTH was set to 1024 according to the current hardware

prototype. The running average used a 12-bit bus-width for µ and (1−µ). However,

the efficiency of the FFT process (98% of data is FFTed), and the issue of a fixed-

width datapath (16 bits) was not considered; these issues do not significantly affect

the performance of the algorithm.

Another difference in the simulating software compared to the process imple-

mented in the prototype is the APB’s initialization stage. Figure 2.11 shows the

state machine of the APB’s initialization process implemented in the FPGA. After

a system global reset, the processor performs a “force-update” mode, using the first

262144 samples to compute an initial mean and average before starting the pulse-

detection cycle. Another simulation study shows that using 12-bit µ and (1−µ), and

a 24-bit datapath results in convergence (with 4% and 13% variation in mean and

variance, respectively) after 20000 snapshots. In this simulation, the first 5 captures,

25

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providing (5 ×16384) / 4 = 20480 snapshots (assuming a decimation of 4) were used

in the the force-update stage. A longer initialization time was prohibited due to the

fact that the the noise environment varied every 5 captures depending on the location

of the aircraft. Furthermore, 5 captures may not be sufficient for the initialization

process since 20000 snapshots convergence assumes as RFI-free environment, whereas

the LISA first 5 captures might contain corrupted samples that could bias the statis-

tics of the data. The second stage of the simulated initialization is performed by

processing the same set of data with the pulse blanker turned on so that the effect of

RFI contributions on the running average computation was minimized. Figure 2.12

illustrates the result of these processes using the first 5 captures of LISA’s channel 7

data. R e s e t W a i t 2 6 2 1 4 4 c l o c k c y c l e sR e s e t A l l F o r c e u p d a t e m e a n / v a r i a n c e S t a r t p u l s e � d e t e c t o r/ p u l s e � b l a n k e r

Figure 2.11: Initialize stage of APB process

2.4.2 Choosing β2, Nblank, and Nwait

Parameters β2, Nblank, and Nwait affect the number of pulses that will be removed

in the blanking process. Nwait and Nblank directly control the size of blanking region.

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(a) Mean (b) Variance

Figure 2.12: Initialization process for the simulation software. The process is slightlydifferent than that implemented in the actual hardware due to the amount of datathat is available. Several jumps in the first stage (force update) corresponds to thepresence of RFI. After 20480 samples (5 captures), the mean and variance converge.The second stage (blanker on) of the process repeats the mean/variance computationwith the blanker turned on to reduce the effect of pulsed RFI.

β2 controls the sensitivity of detection, and can also influence the amount of blanked

data.

β2 and a performance of pulse detection process

To investigate choice of β2, data from LISA channel 7 was examined to determine

the percent of samples exceeding a specified threshold determined by β. Because

the mean measured noise power can be expected to vary significantly throughout a

cross-US flight, the mean and variance estimates used to determine the threshold are

computed by the running average described in Section 2.1. This running average

used Nblank = 2048 and Nwait = 0 in removing pulse contributions from the mean

and variance computations, as described in Section 2.2. These Nblank and Nwait

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result in fairly large blanking region; 1024 samples (representing 51.2 µS of data)

before and after detection point would be removed. This choice seems overestimate

to ensure that all pulses would be completely suppressed in blanking process so that

the mean/variance computation could be accurately determined.

Figure 2.13 illustrates the percent of samples out of the 145 16K captures that

exceed the threshold specified by β2 on the horizontal axis. The dashed line included

in the Figure represents the percent of exponentially distributed noise that would

exceed the same threshold. An exponential, rather than Gaussian, noise distribution

is more appropriate here due to the very short integration time of the incoming

power for this system. In this case, the presence of RFI in the dataset causes the

percent blanked to exceed that of the simulated noise for large β2 values; however

as β2 is reduced, the two curves become more identical because the detection of

noise dominates both processes. In this case, the turning point with the LISA data

appears to be in the range β2 ≈ 30 to 40. Further simulations with other LISA

channels showed β2 values ranging from 40 to 90 to be reasonable; smaller β2 values

clearly led to excessive blanking as should be expected.

Nblank/Nwait and a performance of pulse blanking process

A simulation investigating the effect of Nblank has also been performed. In this

study, two threshold levels are defined: a higher “detection threshold”(β2=90) used

for pulse detection as usual while Nblank is varied. Nwait was kept at “0” at this step.

The output of the blanker is then examined to determine the number of samples

remaining that exceed a lower “reference threshold”(β2=30 using the same mean and

variance computations as those for the detection threshold). This quantity is labeled

Pout in what follows. The total number of samples exceeding the reference threshold

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10 20 30 40 50 60 70 80 900

0.5

1

1.5

2

2.5

3

3.5

4

4.5

β2

Pu

lse

s d

ete

cte

d (

%)

DataEstimation

Figure 2.13: Percentage of samples exceeding threshold for given β2. All 145 capturesof LISA’s channel 7 data are used.

in the original data is also determined, and labeled Pin. The ratio (Pin − Pout)/Pin

then provides information on the effectiveness of the higher threshold blanker with

a given Nblank at removing lower level pulse contributions. Figure 2.14 illustrates

these quantities for a single LISA capture. Note in some cases, samples exceeding

the higher threshold are not blanked in this dataset; this is because such “trigger”

samples may be missed by the subsampled detector if they are less than 4 samples

long.

Figure 2.15 plots (Pin − Pout)/Pin as a percentage (solid curve) from the entire

channel 7 dataset, with Nblank ≥ 1536 which clearly satisfies equation (2.6). The curve

shows only a modest variation with Nblank, and an Nblank value in the range 1536 to

2048 appears appropriate in this case. This can also be interpreted as indicating that

pulsed interference longer than 76.8µsec (i.e. 1536 samples) is not significant in this

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data. Of course, this Nblank parameter will vary for RFI environments dominated

by different types of sources, but the cross-US flight considered here should be fairly

representative of other datasets. Results for smaller Nblank values may show more

sensitivity in Figure 2.15, but are not explored here due to the limitation of equation

(2.6). Data for smaller Nblank values could be generated by varying the FIFO LENGTH

and Nwait parameters, and will be explored in future work.

The saturation of the solid curve at a maximum value of approximately 80%

is caused by the presence of spurious internally generated interference in the LISA

dataset [7]. The dashed curve is computed by redefining Pin and Pout to included

only consecutive sets of 2 or more samples that exceed the reference threshold. In

this case, a removal of approximately 98% of the “low-level” pulse components is

achieved.

0 100 200 300 400 500 600 700 800 9005

10

15

20

25

Po

we

r (d

B)

Time (µs)

InputOutput

Detection Threshold (β2=90)

Reference Threshold (β2=30)

Figure 2.14: Detection Threshold (for detecting pulses) and Reference Threshold (forestimating number of pulses). Nwait=0, Nblank=1536 are used in the APB outputresults shown

30

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2048 2560 3072 3584 409670

80

90

100

Bla

nke

d S

am

ple

s (

%)

Nblank

All pulses exceeding β2=30 threshold

Ignore pulses shorter than 100 ns long

Figure 2.15: (Pin − Pout)/Pin × 100%, where Pin and Pout are the number of sam-ples exceeding the “reference threshold” at input and output of the APB algorithm,respectively.

The possibility of increasing Nwait in order to reduce unnecessary blanking in

the pre-pulse region was investigated. The percentage of blanked samples for Nblank

less than 1024 samples was examined. Blanked samples evaluated from channel 13

data are shown in Figure 2.16. Recall that upon detection, the blanking region

begins at 1024 samples before detection point. Nblank samples after the beginning

of the blanking region would be set to zero. In Figure 2.16, it is clearly seen that

even Nblank of 768 samples is not sufficiency large to blank all corrupted sample.

Consequently, setting Nwait to 768 and Nblank to at least 597 (eq. 2.6) would be

sufficient to successfully remove pulsed RFI in this frequency range.

Figure 2.17 shows the percentage of blanked samples of channels 7–12. Similar

trends can be observed. However, unlike channel 13, channel 7 seems to contain much

longer pulses; the blanker does remove a portion of the pulse even with Nblank of 64.

Due to the somewhat small size of the FIFO (1024 length, representing 51.2 µsec of

LISA data), immediate blanking of the FIFO is preferable. For this reason, Nwait is

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256 512 768 10240

20

40

60

Bla

nked

Sam

ples

(%

)

Nblank

Channel 13

Figure 2.16: Percentage of blanked samples ((Pin − Pout)/Pin × 100%) of LISA’schannel 13 data. The pulses shorter than 100 ns were ignored in this computation.

maintained to “0” to ensure that even such large pulses can still be removed. Future

work can explore a preferred value of Nwait for larger FIFO LENGTH parameter.

2.4.3 Output χ2 test

Now that means for choosing the APB parameters have been established, it is

of interest to quantify the quality of the output data. Because thermal noise power

should approach a Gaussian distribution when integrated sufficiently, the χ2 test

against a Gaussian distribution [8] can be utilized to evaluate if the output of the

blanker satisfies this expectation. However, the entire dataset for a specific channel

cannot be applied in this test, as the mean noise power in a channel will vary as differ-

ent locations are observed. The test was instead performed using sets of consecutive

5 16K captures, all of which were measured within 5 seconds. The χ2 statistic using

4 degrees of freedom was computed using data power integrated over 128 samples in

order to approach the Gaussian distribution.

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256 512 768 10240

50

100

Bla

nked

Sam

ples

(%

)

Nblank

Channel 7

256 512 768 10240

20

40

60

Bla

nked

Sam

ples

(%

)

Nblank

Channel 8

256 512 768 10240

10

20

30

Bla

nked

Sam

ples

(%

)

Nblank

Channel 9

256 512 768 10240

10

20

30

Bla

nked

Sam

ples

(%

)

Nblank

Channel 10

256 512 768 10240

20

40

60

Bla

nked

Sam

ples

(%

)

Nblank

Channel 11

256 512 768 10240

20

40

60

Bla

nked

Sam

ples

(%

)

Nblank

Channel 12

Figure 2.17: Percentage of blanked samples of LISA’s channels 7–12.

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Original data from one set of LISA channel 7 data is illustrated in the upper plot of

Figure 2.18 before and after the APB algorithm is applied with β2 = 40 and Nblank =

2048. The presence of interference in the original dataset results in a high χ2 value

of 262.53, indicating that the data are not likely to be from a Gaussian distribution.

The lower plot illustrates the χ2 statistic of the non-blanked data after blanking

with β2 = 40 versus Nblank, and shows a greatly reduced value compared to the pre-

blanking case. Critical values based on α = 1% and 10% (the probability of incorrectly

classifying a true Gaussian distribution as non-Gaussian) are also illustrated in the

figure. Clearly for this example, the APB output data is much more Gaussian than

the input data, particularly for Nblank exceeding 1366. The poor performance for

Nblank = 1024 is not surprising, since this case does not satisfy equation (2.6), allowing

the possibility that some detected samples remain unblanked.

Simulations from other LISA data subsets show similar results, with a few excep-

tions. In particular, LISA channel 6 (1310 MHz- 1330 MHz) sometimes contains very

strong interference from multiple aviation radars, and a large value of χ2 remains

even after blanking with Nblank up to 4096. The limitation of the fixed FIFO LENGTH

parameter is an issue here, and future work will examine if increasing this parameter

can improve these problematic datasets. However it should certainly be expected that

there are cases with exceptional RFI corruption for which the APB algorithm cannot

retrieve the original noise power.

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0 20 40 60 80 1000

10

20

30

40

Pow

er

(dB

)

Sample Index ( ×1000)

Input (χ2 = 262.53)

Output (Nblank

= 2048)

1024 2048 3072 40960

5

10

15

20

χ2

Nblank

Critical Value(α=1%)

Critical Value(α=10%)

5 × 16K-sample

Figure 2.18: Top: Noise power from 5 captures sucessively taken in the vicinity ofWheeling, IL (approx. 39.87◦ N lat, 80.67◦ W long) at 22,000 ft. LISA is tuned tochannel 7 (1324 - 1344 MHz). Several examples of pulsed RFI are observed. Bottom:χ2 of APB output as Nblank is varied (β2=40, Nwait=0).

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2.4.4 Effect of blanking on integrated spectra

The ideal pulse-blanking algorithm would remove only RFI information, without

changing properties (particularly the mean power level) of the remaining noise infor-

mation. One questionable issue of the APB algorithm is the impact of forcing data to

zero when pulses are detected. This introduces discontinuities into the signal which

may lead to undesired effects on the final output, as well as calibration uncertainties.

Note after the APB operation an FFT is performed in the interference suppressing

radiometer of Section 1.1; clearly the impact of blanked samples on the FFT output

should be investigated.

To examine these effects, APB outputs were processed through FFT and integra-

tion operations. Each 16K LISA capture after blanking was first separated into 32

512 sample “frames” (i.e. a 512 point FFT operation is used). Prior to the FFT, each

frame can be categorized as either BLANK (contains no non-zero samples), NO BLANK

(contains no blanked samples), or PARTIAL BLANK (some samples are blanked), as

shown in Figure 2.19. The FFT is performed on each frame, the power computed in

each FFT bin, and all results in each FFT bin are averaged.

It is clear that the only effect of the BLANK category is to decrease the noise power

level of the final average. It is trivial to correct for this effect simply by counting

the total number of frames and the number of BLANK frames. However, the effect of

the PARTIAL BLANK frames, which contain discontinuities, is more complex. An FFT

operation on such a frame clearly will produce a distorted spectrum and a reduced

noise power level, with the degree of distortion and power reduction related to the

number of blanked samples within the frame. Clearly, narrow band noise sources may

experience some distortion in this process; however, noise sources with bandwidths

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200 250 300 350 4000

10

20

30

40

Po

we

r (d

B)

Time (µs)

InputOutput

BLANK PARTIAL BLANK

NO BLANK

Figure 2.19: Definition of BLANK, PARTIAL BLANK and NO BLANK frames

larger than a few MHz, which are the subject of this work, are not considerably

affected. An example PARTIAL BLANK spectrum is compared to the corresponding

NO BLANK average spectrum in Figure 2.20. The reduction in power level is clearly

visible, along with a moderate distortion in the overall shape of the spectrum. Note

the large power levels observed near the center of this spectrum are due to an internal

DC component of the measured power, not due to external interference.

Various means for coping with the PARTIAL BLANK issue can be conceived. A

simple strategy (called method 1) is to eliminate such frames from the averaging

operation; however this approach may also eliminate a large fraction of the incoming

data in high RFI environments. A second approach to retain these frames while

correcting for the power level reduction is called “method 2: instantaneous scaling.”

By Parseval’s theorem, the effect of blanking on total average power of the frame can

be corrected simply by increasing the power of the computed spectrum by N/Nrem,

where Nrem is the number of non-blanked samples in the frame. This correction is

applied to the power level of each frame before including the frame in the average

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computation. A final approach to is to included all (unscaled) frames in the spectral

average operation, and to maintain a separate count of the total number of non-

blanked time-domain samples included in the average, labeled Ntot, 6=0. Only the final

average power is scaled by Ntot/Ntot, 6=0, where Ntot is the total number of time domain

samples that make up the average operation. This approach is termed “method 3:

slow scaling”.

The upper plot of Figure 2.21 compares average spectra from a single Channel 6

LISA 16K capture before any blanking operations with the averages obtained from

methods 1 and 2. APB parameters β2 = 90 and Nblank = 2048 were used in the

APB algorithm. Both blanking methods are seen to be highly effective in removing

contributions from a pulsed interferer centered at approximately 1315 MHz in this

example. Results from Methods 1 and 2 are also observed to be very similar, demon-

strating that the instantaneous scaling approach is correcting for the reduced noise

power level due to blanking effects. To highlight the differences between methods 1

and 2, the lower plot of the figure illustrates the difference (subtracted decibel values)

between the method 2 (instanteneous scaling) and 1 (NO BLANK only) average spectra.

Differences are generally within 1.5 dB in all cases, and appear noiselike, indicating

that further averaging would likely make these differences less significant.

The difference between averaged spectra for methods 3 (slow scaled) and 1 (NO

BLANK only) is also illustrated in the lower plot; errors from method 3 are observed

to be somewhat smaller than those from method 2 on average. Clearly method 3 is

a simpler operation than that of method 2 (favorable for hardware implementation)

since corrections are required at a much slower rate. In addition, method 3 should

be preferable to method 2 because method 2 allows PARTIAL BLANK frames with a

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great deal of blanking to be weighted equally in terms of the power averaging compu-

tation. However, these frames also have the largest degree of spectral distortion, so

reducing their weight should be advantageous. Figure 2.22 illustrates the mean error

for methods 2 and 3 (compared to method 1) for LISA’s channels 7 through 13 when

averaging results over the entire dataset and the entire frequency spectrum. The

mean error is of interest because it indicates the degree to which the average power

level is not being corrected properly. Results clearly show the method 3 error (slow

scaling) generally to be smaller than that of method 2. A hardware implementation

of method 3 is described in Section 4.3

1310 1312 1314 1316 1318 1320 1322 1324 1326 1328 13300

10

20

30

40

Po

we

r (d

B)

Frequency (MHz)

NO BLANK (Averaged)

PARTIAL BLANK (Single)

Figure 2.20: Frequency spectrum of an example PARTIAL BLANK frame, compared tothe entire NO BLANK average for the corresponding capture

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1310 1312 1314 1316 1318 1320 1322 1324 1326 1328 133015

20

25

30

35

40

Po

we

r (d

B)

Frequency (MHz)

1310 1312 1314 1316 1318 1320 1322 1324 1326 1328 13301

0. 5

0

0.5

1

1.5

Po

we

r (d

B)

Frequency (MHz)

OUTPUT(Inst. Scaled) - NO BLANK

OUTPUT(Slow Scaled)- NO BLANK

Input

NO BLANK

Output(Inst. scaled)

Figure 2.21: Impact of PARTIAL BLANK on the final output. Top: Method 2: Instan-taneous Scaled averages compared to method 1. Bottom: The error of methods 2(inst. scaled) and 3 (slow scaled)

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7 8 9 10 11 12 130.1

0.05

0

0.05

0.1

Channel Index

Mean E

rror

(dB

) Slow ScalingInst. Scaling

Figure 2.22: Mean error in total power estimate from methods 2 and 3 in LISAchannels 7-13

2.4.5 Frequency domain blanking

Although the digital radiometer prototype does not implement RFI mitigation

strategies in hardware after the FFT operation at present, it is of interest to simulate

the expected performance of such approaches. The “channelization” of the FFT

should allow an improved signal-to-noise ratio in detecting pulsed interference within

a single FFT bin. A hardware blanking algorithm could conceivably operate on each

FFT bin in real time by using a strategy identical to that of the APB processor. Such

an approach would allow lower level, rapidly pulsed RFI to be removed if missed by

the original APB.

This algorithm was simulated using the LISA data of 145 captures in channel 6.

After passing each capture through the time-domain APB algorithm with β2 = 40,

and Nblank = 4028, each 16K capture was split into 32 512-point frames. An FFT

operation was then applied to each frame, resulting in a total of 32 × 145 = 4640

temporal samples for each FFT bin. For each bin, a second APB algorithm with

β2 = 90, Nblank = 4, and FIFO LENGTH − Nwait = 2 was then applied to these 4640

samples, with the mean and variance computed by the averaging filter process used

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1310 1315 1320 1325 1330

10

20

30

40

50

Frequency (MHz)

Po

we

r (d

B)

Input (Max Hold)

Input (Average)

1310 1315 1320 1325 1330

10

20

30

40

50

Frequency (MHz)

Po

we

r (d

B)

Output (Max Hold)

Output (Average)

Figure 2.23: Frequency-domain APB algorithm results.

in the original APB algorithm. Figure 2.23 illustrates the average spectra before and

after the frequency domain blanking operation, and shows a slight change in results

near the center of the spectrum. The effect of the blanker is more obvious in the

“max-hold” spectra also illustrated in the plot; “max-hold” refers to the maximum

value of the 4640 temporal samples. Clearly a significant degree of RFI is included

in this dataset near the center frequency 1320 MHz; the frequency domain blanking

operation reduces this interference so that corruption of the average spectrum is less

significant.

2.4.6 χ2-filter: alternative pulse blanking technique

An alternative approach, “χ2-blanking” can also be considered if blanking at a

slower temporal rate is deemed acceptable. In this method, the χ2-test is performed

on data from each bin. If the χ2-value exceeds a specified critical value, samples

in the dataset exceeding a power threshold are removed and χ2 then re-evaluated.

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This iteration is repeated until the distribution satisfies the χ2-test for a specified

critical value. The requirement for a slower temporal rate here is due to the com-

plexity and iterative nature of this algorithm, which is not suited for integration in

hardware. However the algorithm could be applied to already-integrated data as a

post-processing step in software.

2.4.7 Conclusions

Results of the study show the APB approach generally to be effective in reducing

corruption from temporally localized RFI. The simulations performed on the LISA

dataset, while not completely general, arguably should be representative of a wide

range of RFI environments. For these simulations, use of β2 values ranging from 40

to 90 appears to effective, along with Nblank > 1536 (≈ 76.8µsec blanking window)

and Nwait = 0 (immediate blanking). Use of “slow-scaling” of the output power after

blanking was found preferable for correcting errors in the estimate of mean power due

to blanking, and averaged spectra after blanking showed only a modest distortion of

the underlying noise power. Further studies will continue to explore a preferred value

of Nwait for larger FIFO LENGTH parameter, as well as studies with other data sets. In

particular, an airborne version of the digital receiver prototype has been developed

for RFI observations at C-band [9]. Results from flights of this system will provide a

substantial set of RFI observations for further evaluating APB performance in varying

RFI environments.

43

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CHAPTER 3

Sky observation with digital radiometer

3.1 Introduction

The previous chapter describes the pulse blanking process implemented in a digital

radiometer. Clearly, the blanking process can reduce pulsed RFI without significant

error. This chapter mainly concerns deployment of the radiometer for long-term sky

observations to quantitatively verify the functionality of the system as well as the

performance of the blanking algorithm.

3.2 Experimental Setup

The details of the IIP digital back-end were described in Section 1.1. The antenna

used for this experiment is a 10ft-diameter parabolic dish antenna illuminated by a

dual polarized cylindrical feedhorn. A picture of this antenna and the schematic

diagram of an antenna front-end (AFE) is shown in Figure 3.1. The AFE system

is attached to the feedhorn, and includes a switch for measurement of four distinct

ports: internal terminator and noise generator sources as well as the antenna in

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either horizontal or vertical polarization. The AFE also includes filtering and low-

noise amplification stages. Tests showed the antenna ports to be reasonably well-

matched over the band of interest, 1325-1425 MHz, and additional low-loss isolators

are included on the antenna ports to further reduce any reflections. All front-end

components are installed within a temperature-controlled and temperature-monitored

enclosure.

Because the temperature of the internal terminator is monitored, its noise power

can be estimated. The noise diode is used as a noise source with estimated excess-

noise-ratio (ENR) of 27.3 dB. The process to obtain the ENR of the noise diode is

described in the next section.

3.3 Noise diode calibration

The excess noise ratio (ENR) of the noise diode is estimated. The calibration

involves the relation between the noise temperature and the noise power of objects

at known physical temperature.

The noise power, P , emitted by the object having physical temperature, T , is

described by the relation.

P = kTB (3.1)

where P is the noise power (in watts), T is the physical temperature (in Kelvins) of

the object, B is the bandwidth (in Hertz) of observation, and k is the Boltzmann

constant = 1.3807× 10−23m2kgs−2K−1. The relation between noise power generated

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� � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � �� � � � � �� � � ! � " � # $ % & � ' ( ) *+ , - . / � " 0 �� ! � + $ %1 2 3 4 2 5 6 7 8 5 2 3 9 : ; 7 9 5 2 < = 1 2 3 4 2 5 6 7 8 5 2 > 9 : 7 5 9 ? ? 2 <

@ " ( A B 0 � C D E EFigure 3.1: Top: 10ft-diameter parabolic reflector. Bottom: Schematic diagram ofantenna front-end (AFE)

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by each reference load and corresponding physical temperature can be modeled as:

Ph = aTh + b (3.2)

Pn = aTn + b (3.3)

Pc = aTc + b (3.4)

where Ph, Pn, Pc is the noise power (in watts) corresponding to the noise temperature

(in Kelvins) Th, Tn, and Tc. Subscripts “h”, “n” and “c” refer to Hot Load, Noise

Diode, and Cold Load respectively. The coefficient a is proportional to the total gain

of the system while the coefficient “b” accounts for additive noise. The noise power

Ph, Pn, and Pc are measured. The temperature Th and Tc are known. Therefore the

calibration coefficients a, b and the noise temperature Tn of the noise diode can be

determined.

Excess Noise Ratio (ENR) is defined as

ENR = Tn−To

To(3.5)

where To is the room temperature (290 K is assumed). To accurately estimate ENR,

the noise temperature needs to be measured precisely. However, an error due to

the cable loss between switch ports and the loads is unavoidable. The following

experimental setup is designed to account for this error.

3.3.1 Experimental setup for noise-diode calibration

Figure 3.2 illustrates the experimental setup. A matched load “Term-1 (Hot)”

and the noise diode are temperature-controlled at 45 ◦C. The load “Term-2 (Cold)”

was emerged into liquid nitrogen (LN2) whose physical temperature is 77 Kelvin.

The cable connecting switch ports to Term-1 and the Noise Diode are fairly short

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semi-rigid cables whose loss (L1 and L2) are relatively small and can be neglected.

The cable from Term-2 is 1 ft of semi-rigid type and 4.5 ft of RG-58. Tho total loss

is measured to be L3 + L4 = 0.17 + 1.31 = 1.48 dB. Extra attenuators Lx, are to

be inserted between the semi-rigid cable from the switch port and the RG-58 cable

connected to the Term-2 (cold load). The function of Lx attenuators is to provide a

known loss.

N o i s e D i o d eT e r m 1 ( H o t ) L 1L 2L 3T e r m 2( C o l d )L N 2

L xL 4

Figure 3.2: Experimental Setup for noise-diode calibration

Five Lx values were tested. For each Lx value, the ENR of the noise-diode is

estimated. Figure 3.3 shows the relation between Lx and estimated ENR. The total

loss of Term-2’s cable is L3 + L4 + Lx = 1.48dB + Lx. The plot in Figure 3.3 can

be used to estimate the case for Lx = −1.48dB so that its value on vertical axis

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represents a correct ENR at the switch port. Following this procedure yielded an

ENR of ∼ 27.3 dB, averaged over 100 MHz bandwidth (1325–1425 MHz)

0 1 2 3 1028

29

30

31

32

33

34

35

Attenuator Lx (dB)

Est

imat

ed E

NR

of N

oise

dio

de (

dB)

Figure 3.3: Estimated ENR vs Lx pad

3.4 Experiments

Although previous observations of a water pool [3] have indicated successful op-

eration of the APB system, difficulties in calibrating these measurements resulted

due to the large calibration target sizes required for far-field observation of ground

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targets. An alternate experiment was initiated involving sky observations, since it

is expected that the sky provides a slowly varying brightness comprised of known

cosmic background, atmospheric, and astronomical source contributions [10, 11]. If

a high degree of stability of the LISR system can be demonstrated, sky observations

over long time periods should show only a slow evolution as various astronomical

sources enter the antenna pattern, while more rapid brightness variations would indi-

cate RFI effects. Long term sky observations also allow the possibility of calibration,

given that the brightnesses of astronomical targets are reasonably well known. The

sun and moon also provide opportunities for use as calibration targets. Overall the

goal of the campaign is to demonstrate reduction of RFI effects, including calibrated

information on the number of Kelvins of RFI reduction achieved.

At present, calibration using astronomical sources has yet to be achieved. For

this reason, data will be internally calibrated based on the known brightnesses of

the internal noise diode and terminator standards. Consequently, calibrated data

still include antenna and/or other front-end loss effects prior to the front-end switch.

Results are presented in terms of sky brightness temperature integrated over 5.3

seconds, so that high spectral resolution information (100 kHz channels) is available

in the band 1325-1425 MHz.

3.5 Results

Figure 3.4 illustrates the obtained noise temperature of the sky over 6 hours of

observations (00:00 – 6.00am, local time). The top plot shows the time history of

noise temperature over 100 MHz bandwidth. The color scale represents 50 Kelvin to

190 Kelvin, so that various types of RFI can be easily observed; results staying within

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these limits over several hours indicate a highly stable system. Strong interferers are

observed near 1330 MHz, 1400 MHz, 1403 MHz, and other frequencies. The 1330 MHz

source is known to be a local air-traffic control radar, and generates the strong pulsed

RFI observed. The bottom plot in Figure 3.4 shows the average noise temperature

versus time. A 3 Kelvin oscillation due to the radar at 1330 MHz can be noticed from

this plot. The increase in power observed seen at 1:30am corresponds to observation of

the galactic plane. The average noise temperature of at ∼107 Kelvin is contributed

from several factors including component loss and ground contribution, which can

not be eliminated for internal calibration. The narrow-band continuous RFI is also

contribute to this measured value. The latter can be suppressed by cross-frequency

blanking technique described in Section 3.6.2.

Figure 3.5 provides similar plots from the same observation period, but results are

illustrated with the blanker (APB) turned on. A dramatic reduction in the 1330 MHz

source is observed, as should be expected given the pulsed nature of radar emissions.

Other non-pulsed RFI sources are not significantly affected. The average noise power

plot also confirms that the 3-Kelvin oscillation seen in the previous plot is solely

contributed by the radar at 1330 MHz.

3.6 Post-processing mitigation technique

The APB is clearly useful for detecting and removing pulsed RFI in real-time.

However, non-pulsed RFI is of interest as well. The RFI mitigation in this stage are

done in post-processing. Though the real-time mitigation is possible, given the com-

plexity of the algorithm to be implemented, post-processing by software is favorable

at present.

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Figure 3.4: APB-OFF: Top–Spectrogram, color scale represents sky brightness tem-perature in Kelvin. Bottom–Average brightness temperature VS Time. Without RFImitigation some of data are corrupted. Pulsed RFI at 1330 MHz is expected andresponsible for 3-Kelvin oscillation in the average temperature plot.

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T e m p o r a l n o n p u l s e d R F I

Figure 3.5: APB-ON: Spectrogram and average noise temperature plot observed withthe blanker turned on.

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3.6.1 Cross-time blanking

In Figure 3.5, the contribution from temporal non-pulsed RFI can be seen at

1380 MHz. A strategy identical to APB – termed “Cross-time blanking” – can be

applied on time history of each frequency channel. Each of these time histories will be

separated into smaller time-frames and the APB will be applied on each of these time-

frames. Smaller time-frames would preserve slowly varying information, for example

the contribution from the galactic plane centered at 1:30 am. Using larger time-

frames would treat this slow-varying noise power contribution as RFI and remove

them. Clearly, the optimal length of these frames needs to be studied. For this

simulation, frame length of 10 minutes was used, given assumption that there is no

RFI with such a slow varying power.

Figure 3.6 shows the output of cross-time blanking process. The RFI at 0:30 am

and 5:00 am are clearly suppressed.

Figure 3.6: APB-ON with cross-time blanking applied

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Frequency (MHz) Error (Kelvin) Frequency (MHz) Error (Kelvin)1400 9.59 1336 0.191403 5.30 1353 0.181333 0.49 1347 0.101350 0.47

Table 3.1: Contribution of narrow-band RFI to the average noise power (ordered bycontributed error)

3.6.2 Cross-frequency blanking

Though the average brightness plot in Figure 3.6 is considerably improved com-

pared to the data without any RFI mitigation applied (Figure 3.4), the contribu-

tion of narrow-band RFI can not be neglected. In fact, some of these interference

greatly contribute to the average noise power. Table 3.1 tabulates the contributions

of narrow-band RFI at some specific channels.

Cross-frequency blanking involves comparisons of results in different frequency

bins, in order to detect and remove more time continuous RFI sources. Again the

long time scales expected for sources in this process did not motivate immediate

consideration for implementation in hardware. A simple software based algorithm

has been developed. Three different approaches to detect this type of RFI have been

proposed. The first method (Xfreq-I) uses a simple threshold based on average

noise temperature over the 100 MHz band (Figure 3.7). However, without external

calibration, the instrument gain pattern over frequency limits the sensitivity of this

algorithm. As a result, weak RFI can not be detected.

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1340 1360 1380 1400 1420

80

100

120

140

160

180

200

Noi

se T

empe

ratu

re (

Kel

vin)

Frequency (MHz)

Data7σ−threshold3σ−thresholdRFI

Figure 3.7: Xfreq-I cross-frequency RFI detection algorithm. A simple threshold isderived from the average noise temperature over the 100 MHz spectrum. Th possiblelowest threshold (dashed line) is limited by the gain pattern of the instrument.

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An alternative approach (Xfreq-II) is to utilize thresholding the derivative of

the data in frequency. This method is expected to remove the gain pattern ver-

sus frequency (Figure 3.8(a)). This is important because if such an algorithm were

implemented in hardware, it is unlikely that calibrated data would be utilized in

the algorithm. Xfreq-II method provides a means to improve detection sensitivity

comparing to the first approach; however, any ripple in the gain pattern also intro-

duces non-negligible derivative. This becomes the limitation of the derivative-based

thresholding approach.

1320 1340 1360 1380 1400 1420 14400

10

20

30

40

50

60

70

80

Der

ivat

ive

of N

oise

Tem

pera

ture

Frequency (MHz)

Data1σ−thresholdRFI

(a) Derivative of the data in frequency

1340 1360 1380 1400 1420

80

100

120

140

160

180

200

Noi

se T

empe

ratu

re (

Kel

vin)

Frequency (MHz)

DataRFI

(b) Detected RFI (marked with ’×’)

Figure 3.8: Xfreq-II cross-frquency RFI detection algorithm. The threshold is de-rived from the derivative of the data. This allows weak RFI to be detected.

Though not hardware-friendly, another algorithm designed to be insensitive to the

instrument gain pattern versus frequency has been investigated. For this approach

(Xfreq-III), the gain pattern is first estimated and then used to define a frequency

varying threshold. The gain pattern is estimated by interpolating local minima of the

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frequency response curve as illustrated in Figure 3.9(a). The algorithm developed was

also enhanced to incorporate information from the cross-time post-FFT algorithm in

order to improve detection of corrupted FFT bins.

1330 1331 1332 1333 1334 133570

80

90

100

110

120

130

140

150

Frequency (MHz)

Noi

se T

empe

ratu

re (

Kel

vin)

DataLocal minimaEstimated uncalibrated gain pattern

(a) Gain pattern estimation

1340 1360 1380 1400 1420

80

100

120

140

160

180

200

Noi

se T

empe

ratu

re (

Kel

vin)

Frequency (MHz)

DataThresholdRFI

(b) Detected RFI (marked with ’×’)

Figure 3.9: Xfreq-III cross-frequency RFI detection algorithm. Gain pattern is firstestimated by interpolating local minima of the frequency spectrum (Left panel). Thisallows the contribution of uncalibrated instrument gain pattern to be considered inthe detection process.

Figure 3.10 shows the final results after incorporating all RFI mitigation tech-

niques: real-time APB, cross-time, and cross-frequency blanking. Clearly, the cross-

frequency blanking helps to remove biasing due to narrow-band interference, and

results in several Kelvins improvement in the average noise temperature estimation.

Note that remaining variation across the frequency is a result of the gain pattern of

the instrument before the calibration point. Calibration using external sources as

reference loads are required to eliminate this effect.

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Calibration corrections for cross-frequency blanking again are simple to imple-

ment, involving simply scaling by the number of frequency channels retained relative

to the original number of channels. Note at L-band that high spectral resolution is

useful in removing contributions from hydrogen line emissions at 1413 MHz, as these

narrowband contributions can influence the accuracy of sea salinity retrievals from

space [11].

Figure 3.10: APB-ON with cross-time and cross-frequency blanking applied.

3.6.3 Temperature control and stability of the system

The average noise temperature plot in Figure 3.10 demonstrates very stable system

for at least over 6 hrs observation period. However, small variation, ∼ 1 Kelvin, can

be noticed. This error is a result of temperature drift of the analog down-converter

back-end. The additional hardware required for temperature monitoring/controlling

are disscussed in Section 4.4. Installation, calibration and possible improvement are

also discussed in the Sectin 4.4 as well.

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3.6.4 Narrow-band interferences at 1400 and 1403 MHz

Among different narrow-band interference observed from the sky measurement

(Table 3.1), the ones at 1400 and 1403 MHz are of most interest. Both of them are

relatively strong, narrow, fairly constant over time and appear inside the protected

spectrum (1400-1427 MHz) Several brief experiments were done to investigate some

properties of these sources.

1400 MHz RFI

A simple survey was done over the roof-top of the ElectroScience laboratory.

The experimental setup consists of an L-band square horn antenna and a spectrum

analyzer. The antenna has been focused to different directions, approximately, North,

West, South, East and straight up to the zenith. Snapshots when the antenna was

pointing East and South are shown in Figure 3.11. No RFI at this frequency was

detected from pointing toward South as well as in other directions. This suggests the

1400 RFI could be partly contributed from external sources.

A possibility that this source is a harmonic of other signals was investigated.

The source at 700 MHz can be observed by spectrum analyzer located at ESL. The

multi-window tracking feature of the spectrum analyzer over an extended time period

reveals that the 1400 MHz source is not likely to be a second harmonic of this 700 MHz

source. The external source of RFI at this frequency remains unknown; however, it

was found that the radiation from radiometer back-end contributes to this RFI as well.

In fact, a tone at 700 MHz as well as 1400 MHz can be observed by the spectrum

analyzer. Figure 3.12 shows the frequency spectrum of 50 kHz span around the

center frequencies at 700 MHz and 1400 MHz. One feature of the spectrum analyzer

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M a x .A v g .M i n .

(a) East

M a x .A v g .M i n .

(b) South

Figure 3.11: A survey of 1400 MHz RFI. The antenna was focused to different direc-tions. Strong RFI is found from the East while there was no other RFI received fromother directions.

allows the spectrum at two distinct frequencies to be observed simultaneously. An

antenna was pointing toward the radiometer back-end rack. With the radiometer

turned off, Figure 3.12(a) shows a source at 700 MHz while no 1400 MHz source

had been observed. With the radiometer turned on, Figure 3.12(b) illustrates strong

contribution at 700 MHz as well as its harmonic at 1400 MHz.

1403 MHz RFI

Though the major sources of these RFI are still undetermined, most of them

are likely to be from radiation from local equipment including the radiometer itself.

The survey at ESL’s roof does not detect RFI at this frequency from any direction.

This reduces the possibility of external contributions. However, the strong 1403 MHz

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5 0 k H z 5 0 k H z(a) With radiometer turned off

5 0 k H z 5 0 k H z(b) With radiometer turned on

Figure 3.12: Multi-window plot show the spectrum centered around 700 MHz and1400 MHz measure at the same time. The antenna is directed into the radiometerback-end rack.

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RFI are found to be radiated from the radiometer back-end and spectrum analyzer

themselves (Figure 3.13).

3.6.5 Conclusions

Current results indicate that the LISR system is providing stable sky observa-

tions, although a calibration using external targets will be required to reduce the

gain pattern versus frequency. The studies show the APB to be effective at reduc-

ing the influence of a local air traffic control radar, as well as the advantage of high

spectral resolution in removing narrowband temporal/continuous RFI sources. Both

of these suppression techniques are possible due to the use of a digital receiver, as

the rapid temporal processing and large number of FFT bins is only practical with

such a system, and the real-time processing achieved allows a manageable data rate

to be retained ultimately. Future works include long-term observations with exter-

nal calibration and improving the stability of the analog down-converter with more

sophisticated temperature control.

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1 0 0 k H z(a) Antenna pointed toward spectrum analyzer(0◦ relative)

1 0 0 k H z(b) Pointed away (90◦ relative)

1 0 0 k H z(c) Pointed away (180◦ relative)

1 0 0 k H z(d) Pointed away (270◦ relative)

Figure 3.13: 1403 MHz radiation from spectrum analyzer. Observation at 1403 MHzwith a spectrum analyzer also found the RFI leaked from the instrument itself

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CHAPTER 4

Hardware issues for implementation in FPGA Components

The first prototype of the L-band Interference Suppressing Radiometer-1 (LISR1)

was completed by late September 2002. This compilation of the radiometer included

only one ADC and therefore sampled only a 50 MHz bandwidth, but retained the

full DIF, APB, FFT, and SDP operations with a few exceptions. The FFT operated

at 14% duty cycle and APB processor has only one BTR implemented. A second

prototype, LISR2, was developed to remove the limitations of LISR1. A higher-

density series of FPGA components were used (the Altera “Stratix” line). This

allows 6 parallel FFT processors to be included so that 98% duty cycle computations

resulted; four BTRs are included in this version. A photo of LISR2 digital backend

is shown in Figure 4.1. Both these prototypes were completed prior to the efforts of

this thesis.

For both prototypes, the digital processing units (DIF, APB, FFT and SDP) are

programmed into 3 cascaded cards, each of which are controlled by a computer via

ethernet. Having digital processing units on separate cards makes implementation

of the scaling process more difficult. However instantaneous scaling could be im-

plemented in a single FPGA receiver if a higher-density FPGA were provided. As

mentioned in Section 2.4.4, instantaneous scaling at a high rate should be avoided.

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Figure 4.1: LISR2 digital backend; the vertical cascade of three circuit boards nearthe left hand side contains the dual ADC sections (upper and lower boards) and thedigital channel combination and filtering (DIF) section (center board). The APBsection for removing temporal pulses is also implemented on the center board. Fol-lowing the vertical cascade to the right is the FFT processor, then the SDP section forpower computation and integration operations. Finally a “capture card” provides theinterface to the PC. Microcontrollers are also included on each card (the smaller at-tached circuit boards with ethernet cables) to enable PC setting of FPGA parametersthrough an ethernet interface.

Slow scaling, in contrast, simplifies hardware design and requirements. This process

requires information on the number of samples set to zero by the blanking stage,

which is carried over from the APB unit. Accordingly, a final prototype, LISR3, was

developed to resolve these communication issues. All the DSP units are now inte-

grated into a single FPGA. The large size of the program on this FPGA raises many

concerns on simulation and programming, including clock management and low-level

signal routing. Clock distribution for a Stratix FPGA is reviewed in Section 4.1, while

Section 4.2 deals with the signal-routing issue in detail.

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4.1 Clock management for LISR3’s FPGA

Figure 4.2 illustrates the LISR3 digital back-end. The clock-generator (upper-left

corner of the figure) provides a 200 MHz clock for the ADC cards. The 10-bit data

sampled by the ADC device (Analog Device’s AD9410) are synchronized to the 100

MHz clock generated by the ADC itself. The ADC card also includes one PLD –

programmable logic device – to allow the AD9410 to be synchronized to an external

clock if needed. However, this function is not utilized in the LISR3 design, and this

PLD simply serves as a data and clock output-buffer. Even though registered through

the PLD, noticeable signal degradation can be observed in both the clock and data

lines (Figure 4.3). Figure 4.3 also points out that a distorted clock signal could affect

the duty cycle of the clock pulse seen by the following device. The measurement

was performed with the Stratix card attached (i.e. normal operating setup as in

Figure 4.2) excepted only DIF circuits were loaded into the Stratix processor. The

problem is expected to be even more critical if the entire processor (DIF, APB, FFT

and SDP), introducing much higher loading, is programmed.

To prevent transmission errors, including loss of synchronization and bit-errors,

the clock signals are regenerated and all data are registered immediately after inputs

of the Stratix FPGA.

The data from each ADC is first re-synchronized to its own clock, then syn-

chronized with the “master” clock derived from one of the two ADC cards. Since

the properties of both clocks are identical, the choice of master clock is decided

from the signal-routing and layout perspective (Section 4.2). The master clock is a

100-MHz ADC clock, regenerated by Stratix’s Enhanced-PLL (dedicated phase-lock

loop circuits available in Stratix devices) immediately after the input pin. Stratix’s

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Figure 4.2: LISR3 digital backend; underneath a heat-sink on the center board isthe Stratix FPGA which contains the entire processor. The boards on the left andright of the Stratix board are the ADC cards. The system clock used to run theStratix processor is also derived from one of these ADC cards. Above the Stratixcard is a “capture card” providing a 256K-FIFO buffer and the interface to the PC.Only a single Microcontroller card (the smaller card attached to the bottom of theStratix board) is needed in this prototype. Except for the Stratix card, the otherparts of the hardware are the same as in LISR1 and LISR2. The clock-generator andpower-supply (top-left and bottom-right respectively) are also the same as in previousprototype.

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0 10 20 30 40 50

0

2

4

Time (ns)

Vol

tage

(V

) Clock (Stratix)Clock (A/D Card)Min. HighMax. Low

0 10 20 30 40 50

LowHigh

LowHigh

Time (ns)

(a) Clock

0 10 20 30 40 50

0

2

4

Time (ns)

Vol

tage

(V

) Data (Stratix)Data (A/D Card)Min. HighMax. Low

0 10 20 30 40 50

LowHigh

LowHigh

Time (ns)

(b) Data

Figure 4.3: Clock and data signal at the output of ADC cards (dashed line) beforecrossing the SCSI-3 connector to the Stratix card (solid line). The minimum high(1.7 V) and maximum low (0.7 V) voltage level according to LVTTL/LVCMOS re-quirement are also shown as a reference. The lower plots demonstrate the expectedlogic level to be recognized by the Stratix FPGA according to these reference levels.

Enhanced-PLL is capable of driving up to 6 clock networks. For the LISR3 design,

only two of the PLL output are enabled, supporting two global clock networks. A

single clock network indeed suffices; however, in this design, the APB and Microcon-

troller interface are driven by separate clock networks for design convenience.

In order to synchronize the data to the master clock, a simple dual-clock FIFO is

utilized. Quartus R©II design software provides a tool to implement single-clock/dual-

clock FIFO’s. Figure 4.4(a) illustrates data and control signals configured for this

purpose. wrreq (write-request) and rdreq (read-request) provides a means to control

the FIFO writing and reading processes independently. wrclk (write-clock) and rdclk

(read-clock) allows the FIFO to be written and read from separate clock domains.

The state-machine in Figure 4.4(b) controls the initializing process of each dual-clock

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FIFO to ensure that the FIFO is approximately half-full before the first reading cycle

is allowed. Figure 4.5 illustrates the whole clock regeneration scheme.d a t a [ 1 5 . . 0 ]w r r e qw r c l kr d c l kr d r e q q [ 1 5 . . 0 ](a) Configuration

0 01 01 1 w a i t 1 2 c l o c k � c y c l e sw a i t 4 c l o c k � c y c l e s0 0 � I n i t i a l i z a t i o n1 0 � A l l o w w r i t i n g ( w r e n a = 1 )1 1 � A l l o w w r i t i n g ( r d e n a = 1 )

(b) State machine control initialization process

Figure 4.4: Dual-clock FIFO.

4.2 Floorplan and Signal-Routing

Figure 4.6 shows the layout of the latest Stratix FPGA compilation. The yellow-

shaded area represents allocated logic elements and dedicated circuits. Even though

only half of the resources are occupied, the resource allocation and signal-routing

need to be carefully determined to meet timing requirement for operation at 100

MHz. Figure 4.7 shows the floorplan of the FPGA. The labeled area shows the

LogicLockTMregion, a set of constraints used by Quartus R©II design software in order

to lock down design into specified regions in the device’s floorplan. The figure also

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d a t aw r r e qw r c l kr d c l kr d r e q qM a s t e r C l o c kP L LA D C � c a r d( U p p e r b a n d )1 0 + b i t d a t a1 0 0 M H z c l o c k D u a l � c l o c k F I F O ( U p p e r )

S y n c . d a t a ( U p p e r )d a t aw r r e qw r c l kr d c l kr d r e q qM a s t e r C l o c kP L L

A D C � c a r d( L o w e r b a n d )1 0 + b i t d a t a1 0 0 M H z c l o c k D u a l � c l o c k F I F O ( L o w e r )S y n c . d a t a ( L o w e r )

Figure 4.5: Clock regeneration and synchronization scheme for Stratix FPGA

shows the interfaces to the other parts of the system. Limited by the physical ori-

entation (Figure 4.2), the data and clock from ADC cards are fed from the top and

bottom sides of FPGA. The clock signal from the lower part of the chip is designated

as a master clock since it can access the global clock network more effectively. The

nearest PLL to the clock input from the upper part of the chip is much further away

(also far from the clock output pin) and would cause additional propagation delay in

the clock line.

The “Rabbit”’s Microcontroller is linked to the Stratix FPGA via I/O banks on

the left of the floorplan while I/O banks on the right are allocated for data and clock

outputs. Therefore, in order to minimize routing expenses, the data flow should be

from the upper, lower and left of the FPGA to the output pins on the right of the

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Figure 4.6: Final layout of Stratix FPGA. Yellow-shaded area shows occupied re-sources.

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D I F( L o w e r )

D I F( U p p e r )F F TD at aFIFO I nt egrat or

A / D c a r d ( U p p e r b a n d )

O ut put

A / D c a r d ( L o w e r b a n d )

C omput er C ont rolP ower C omput ati on O ut put R egi st ers

Figure 4.7: Floorplan of the Stratix FPGA. All marked/labeled areas are assigned asLogic-Lock region. During “fitting” process, the simulator will attempt to allocateresources according to these given criteria. The connections to the rest of the systemare also shown.

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floorplan. The layout seems to follow this flow with the exception of FFT module.

The FFT is placed on the rightmost of the FPGA next to the output pins. The choice

of FFT location is bounded by availability of DSP blocks. Stratix provides several

dedicated circuitry – memory blocks, DSP blocks and dedicated clocks – which are

assigned to specific locations. Though it is not required to confine each module into

the same block as those dedicated circuits, it is preferable to minimize the routing

resources needed.

Figure 4.8 shows the location of two columns of DSP blocks, used extensively

by the FFT and APB as a built-in multiplier circuit. Each Stratix device has 14

DSP blocks, 7 blocks per column. One DSP block can implement up to either eight

full-precision 9 × 9-bit multipliers, four full-precision 18 × 18-bit multipliers, or one

full-precision 36 × 36-bit multiplier with add or subtract features. One FFT processor

requires 1 block of DSP, therefore one entire DSP column must be assigned to the

FFT processors. The FFT module also places heavy demands in other resources

(Figure 4.6); it is not possible to place this module close to the left column of the

DSP where non-negligible portion of those resources have to be allocated for the

Microcontroller interface module as well.

FFT placement eventually constrains the possible location assignment for the rest

of the circuits. APB also requires DSP blocks for multiplication processes; the APB

has to be placed around the other column of the DSP resources. DIFs must be

close to the input pins to minimize routing delays. The APB also requires numerous

M512-RAM blocks (Figure 4.9). Hence, the top-left and bottom-left regions of the

floorplan become the perfect location for them. Finally, the location o the SDP (power

computation and integrator) as well as the output registers are fixed, forcing the rest

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P L L

M 4 K R A M

D S PFigure 4.8: DSPs and M4K-RAM locations in Stratix FPGA. The location of PLLused to generate the “master clock” for the entire chip is also shown.

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M 5 1 2 R A M

M � R A M ( n o t u s e d )Figure 4.9: M512-RAM and M-RAM locations in Stratix FPGA

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of the data flow to reside along the center of the chip. The final design does not

utilize any M-RAM blocks. An alternative FPGA model that has less or no M-RAM

blocks can be proposed for future work to trade the unused portion of floorplan for

other useful resources.

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4.3 Implementation of slow scaling process

The scaling process is accomplished by a combination of hardware and software

operations. The FPGA counts the number of blanked samples, then attaches this

number to the data stream. The controlling software extracts that information from

the received data block, computes the scaling factor and then finally corrects the

data.

The most important issue of the blanking counter design is to ensure that the

number attached to each data block represents the correct number of zeroed samples

within that block. Figure 4.10 shows the block diagram of the counter module and

associated units. The “Delay Register” is inserted between the blanking counter and

the 1-bit blank signal generated by the APB. The number of clock cycles delayed by

this register must be exactly the same as the total delay for data to go through the

FFT, power computation, and integration processes. Circuit analysis is required to

provide the number of delay cycles needed.

S D PT oC a p t u r e c a r dA P B

F F TB l a n k i n gC o u n t e r

P o w e rC o m p u t a t i o n" B l a n k " o r " D o n o t b l a n k "D e t e c t o rB l a n k e rF r o mD I F s D e l a yR e g i s t e r s I n t e g r a t o r

Figure 4.10: Detailed block diagram of APB and SDP.

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Figure 4.11 illustrates test results. The plot show average power of integrated

spectra in blanker-off mode (solid line). The pulsing behavior is known to be pulsed-

RFI from the local radar. The blanker-on mode removes these pulses but also drops

the power level down several dB. The blanker-on with scaling process corrects for this

error and moves the power level back to the correct value, the average noise-power

without RFI.

B l a n k e r O F FB l a n e r O N ( n o s c a l i n g )B l a n k e r O N ( w i t h s c a l i n g )4 5 . 4 84 5 . 4 64 5 . 4 44 5 . 4 24 5 . 4 04 4 . 3 84 4 . 3 64 4 . 3 44 4 . 3 24 4 . 3 0 1 0 2 4 2 0 4 8 3 0 7 2 4 0 9 6Figure 4.11: Slow-scaling test results

4.4 Thermal Control for Analog Down-converter

A thermal control and measurement system was developed for the front-end unit,

in order to enhance system gain stability as well as to provide accurate control of

internal calibration load noise powers. The down-converter unit was initially designed

to operate without any thermal control; however, the results from early phases of

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sky observation have revealed some signs of system gain stability problems. Tests

confirmed that the thermal control system for the front-end performs adequately

at keeping the front-end average temperature stable to within approximately 0.2 K

over long periods of time. The performance of the digital back-end should be only

minimally affected by temperature drift. Hence, issue of gain stability of the analog

down-converter, and its need for thermal control have been reviewed.

Figure 4.12 shows the temperature measured at the enclosure of the down-converter.

The plot shows a strong correlation with ambient temperature as expected. Fig-

ure 4.13 plots the power observed at switch ports of the front-end for 17 hrs. System

gain variations, appear as slow fluctuations in power level that can be noticed in

every switch port. Finally, Figure 4.14 compares the power plot in Figure 4.13 and

temperature plot in Figure 4.12; a strong correlation can be noticed.

18:00 21:20 00:40 04:00 07:2020

25

30

35

Time (HH:MM)

Tem

pera

ture

(° C)

Down−ConverterAmbient

Figure 4.12: Temperature of down-converter compared to the ambient temperature.

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20:42 23:23 02:03 04:44 07:2535

40

45

50

55

Pow

er (

dB)

Time (HH:MM)

Noise DiodeTerminatorAntenna (H−Pol)

Figure 4.13: Power at distict ports of the front-end switch: shown are Noise Diode,Terminator and Antenna (H-Pol). The front-end temperature is set to 45 ◦C

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18:10 18:51 19:31 20:11 20:51 21:3236

37

38

Rel

. Pow

er @

ant

enna

por

t (dB

)

Time (HH:MM)18:10 18:51 19:31 20:11 20:51 21:32

25

30

35

Dow

n−C

onve

rter

Tem

p. (°

C)

Figure 4.14: Comparison of the power seen at antenna port and the tempearuture ofdown-converter.

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To stabilize the down-converter inside temperature, a commercial thermal control

unit was purchased for use with a resistive-heater mounted on the enclosure’s inside

wall. The controller can be operated at 120 VAC. Its solid state relay output pro-

vides a current drive up to 15 amperes; the resistive-heater is chosen as a heating

component. The controller can be configured via the RS232 communication port of

computer. A simple software was also developed to monitor and record the tem-

perature during the measurement. The controller can be set for different modes of

operations, including on-off and PID control modes. On-off mode with adjustable hys-

teresis provides a simple solution for thermal control by switching on the output relay

if the input temperature is below the set point, or switching off the output otherwise.

In this mode, a “Dead-band control” parameter controls the minimum temperature

offset that can trigger the output switch. PID modes provide more control of the

heating process with the expense of algorithm complexity and a complicated calibra-

tion. Controller tuning parameters include “proportional bandwidth (P)”, “integral

gain (I)” and “derivative rate (D)” allowing the controller can be configured for P,

PI, PD or PID modes.

For the sky-observations in Chapter 3, the thermal control system was set in P

mode. Thermal insulators were added on every side of the enclosure, except the front,

to enhance the heating efficiency. The front panel of the enclosure is uncovered to

provide thermal ventilation if necessary (Figure 4.15(a)). The temperature set point

is at 45◦, a few degrees higher than the average temperature of the enclosure without

heating. The heater was fixed to one of the side wall of the enclosure (Figure 4.15(b)).

Two temperature probes were placed in the enclosure. The “Control” probe – used

to control heating process – was placed on the heater to ensure the most sensitive

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D o w n � c o n v e r t e rT h e r m a l i n s u l a t o r

(a) Thermal insulation plan for a down-converter

(b) Top view of a down-converter enclosure: the lo-cation of control probe/heater (round marker) andmonitor probe (squared marker) are shown. Di-mension are in inches.

Figure 4.15: Thermal insulation plan and location of the temperature probes

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control. A “Monitor” probe was located at the opposite corner of the enclosure

to provide rough information on temperature gradient on the heating surface. The

temperature history of down-converter for sky-observations is shown in Figure 4.16.

Figure 4.16(a) shows the success of thermal control process. Although Figure 4.16(b)

highlights the issue of temperature gradients, the results in Chapter 3 show adequate

performance of this temperature control system. Further improvement may includes

operating in PI mode to remove the temperature offset (from the set point at 45

◦C) and to further improve temperature stability. Reference [12] suggests a more

sophisticated temperature control technique that can be applied to this system as

well.

4.4.1 Conclusions

An integration of digital processing units into a single Stratix FPGA was com-

pleted. Several concerns need to be carefully considered, including clock distribution,

resources allocation, and signal-routing in physical level. Clock regeneration and re-

synchronization are required to ensure the quality of the master-clock signal, and to

synchronize the data stream from two ADCs. Routing delays must be minimal to

meet the timing requirement at 100 MHz operation. Blanking counter has been em-

bedded in FPGA to provide the number of blanked samples for slow-scaling process

(currently performed in software). An issue of thermal control has been addressed to

improve the system stability for long-term measurement. Current solution employs

PID controller operating in P mode. Thermal insulator was added to help improving

thermal stability. As a result, the temperature of down-converter enclosure appears

to be stable within 0.2 ◦C (at control point) for 6 hours of observation; however, the

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0:04 1:32 3:01 4:29 5:5847.1

47.2

47.3

47.4

Time (HH:MM)

Tem

p. (°

C)

(a) “Control” probe

0:04 1:32 3:01 4:29 5:5843.5

44

44.5

Time (HH:MM)

Tem

p. (°

C)

(b) “Monitor” probe

Figure 4.16: Temperature of down-converter enclosure for sky-observation in Chap-ter 3. The temperature measured from “Control” probe is used as an input forcontrolling feed-back loop.

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temperature gradients on the controlled enclosure can be noticed. The future works

will improve the thermal-control plan to reduce the temperature gradients. Further

system refinement is also of interest. The possible areas include integrating the digital

back-end unit onto a single circuit board to reduce signal degradation. Implemen-

tation of the entire slow-scaling process and cross-frequency blanking process in the

FPGA would be useful once higher-density FPGA is available.

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CHAPTER 5

Conclusions

Performance of the APB algorithm has been studied with the simulation software.

With a few exceptions, the simulation software was designed to be identical to the

actual processes implemented in hardware. APB initialization stage is one of the

processes that cannot be identically realized in software. APB initialization requires

a large amount of data to be processed. However, the results show the simulating

process to have comparable performance comparing to the actual hardware. Results

of the study show the APB approach generally to be effective in reducing corruption

from temporally localized RFI. The simulations performed on the LISA dataset, while

not completely general, arguably should be representative of a wide range of RFI

environments. For these simulations, use of β2 values ranging from 40 to 90 appears

to be effective, along with Nblank > 1536 (≈ 76.8µsec blanking window). Use of “slow-

scaling” of the output power after blanking was found preferable for correcting errors

in the estimate of mean power due to blanking, and averaged spectra after blanking

showed only a modest distortion of the underlying noise power. APB-like algorithm

applied on a single FFT bin, later termed as the “cross-time” blanking technique, has

been studied with the same dataset.

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Results from sky measurements indicate that the LISR system is providing stable

sky observations. The studies show the APB to be effective at reducing the influence

of a local air traffic control radar, as well as the advantage of high spectral resolution in

removing narrowband continuous RFI sources. Both of these suppression techniques

are possible due to the use of a digital receiver, as the rapid temporal processing and

large number of FFT bins is only practical with such a system, and the real-time

processing achieved allows a manageable data rate to be retained ultimately. Several

approaches for non-pulsed RFI blanking have been proposed. The advantages and

disadvantages of each method were highlighted. Future works will investigate the

performance and limitations of each approach in further detail.

LISR3, the new prototype that has all digital signal processing units embedded in

a single FPGA device, was completed. Issues of clock distribution and signal-routing

have been addressed to ensure that the timing-requirement of 100 MHz operation

is achieved. Temperature stability of the analog down-converter section has been

improved. The feasibility of incorporating the cross-time and cross-frequency blanking

techniques into a hardware will be investigated in future studies.

The major contribution of this thesis is to quantitatively verify the performance of

the APB technique from software simulations and actual measurements. The results

also show the gains that can be achieved through simple temporal and spectral RFI

removal techniques. It is certain that incorporation of digital receiver technologies into

passive remote sensing systems will increase in the future, as such technologies can

dramatically improve the capabilities of such systems in mitigating RFI. This study

provides the support that APB is a good candidate for such systems. In addition,

digital design also allows the size of prototype/product to be more compact. The

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current prototype digital receiver combines all the RFI processing stages onto a single

FPGA. Market research also shows that rad-tolerant FPGA components of similar

size and performance are already available for space operations.

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APPENDIX A

LISA’s frequency channels

Channel Frequency range (MHz) Channel Frequency range (MHz)1 1240 – 1260 8 1338 – 13582 1254 – 1274 9 1352 – 13723 1268 – 1288 10 1366 – 13864 1282 – 1302 11 1380 – 14005 1296 – 1316 12 1394 – 14146 1310 – 1330 13 1408 – 14287 1324 – 1344 14 1422 – 1442

Table A.1: Frequency range of LISA’s channel 1 to 14

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BIBLIOGRAPHY

[1] S. W. Ellingson, “Characterization of some L-band signals visible at Arecibo,”tech. rep., The Ohio State University ElectroScience Laboratory, Febuary 2003.

[2] S. W. Ellingson and G. A. Hampson, “RFI and asynchronous pulse blankingin the 1230-1375 mhz band at Arecibo,” tech. rep., The Ohio State UniversityElectroScience Laboratory, Feb 2003.

[3] G. A. Hampson, J. T. Johnson, and S. W. Ellingson, “Design and demonstra-tion of an interference suppressing microwave radiometer,” in Proc. 2004 IEEEAerospace Conference, 2004.

[4] S. W. Ellingson and G. A. Hampson, “Mitigation of radar interference in L-bandradio astronomy,” ApJS, vol. 147 (1), pp. 167–176, 2003.

[5] S. W. Ellingson, G. A. Hampson, and J. T. Johnson, “Characterization of L-band RFI and implications for mitigation techniques,” in Proc. 2003 IGARSS,Toulouse, July 2003.

[6] S. W. Ellingson and J. T. Johnson, “Airborne RFI measurements over the Mid-Atlantic coast using LISA,” tech. rep., The Ohio State University ElectroScienceLaboratory, January 2003.

[7] J. T. Johnson and S. W. Ellingson, “Airborne RFI measurements using LISAduring transit to and in the wakasa bay campaign,” tech. rep., The Ohio StateUniversity ElectroScience Laboratory, July 2003.

[8] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, NumericalRecipes in C. New York: Cambridge University, 1992.

[9] J. T. Johnson, A. J. Gasiewski, G. A. Hampson, S. W. Ellingson, R. Krishna-machari, and M. Klein, “Airborne radio frequency interference studies at C-bandusing a digital receiver,” in Proc. 2004 IGARSS, vol. 3, pp. 1687–1690, Anchor-age, Alaska.

[10] J. Y. Delahaye, P. Gole, and P. Waldteufel, “Calibration error of L-band skylooking ground-based radiometers,” Radio Science, vol. 37, pp. 11/1–11/11, 2002.

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[11] D. M. LeVine and S. Abraham, “Galactic noise and passive microwave remotesensing from space at L-band,” IEEE Trans. Geosc. Rem. Sens., vol. 42, pp. 119–129, 2004.

[12] R. Villarino, J. Fernandez, A. Camps, M. Vall-llossera, I. Corbella, N. Duffo, andF. Torres, “Design and test of the L-band AUtomatic RAdiometer (LAURA)temperature control,” in Proc. 2005 IGARSS, Seoul, Korea, 2005.

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