0418_6

Copyright 2011 Agilent Technologies

System Performance Evaluation of Advanced Radar Systems through Simulation and Test

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SystemVue Radar Features Radar Design Challenges and Solutions Platform for simulation and Testing

Radar Models Signal Source Environment Transmitter / Receiver Pulse compression / Pulse Doppler processing / CFAR Measurement

Application Examples Pulse Doppler (PD) Digital Array (DAR) Ultra Wideband (UWB) Frequency Modulation Continue Waveform (FMCW)

Radar Component Test Instrument Links Application examples

Agenda

Modern radar systems require precise and robust electronic design in order to perform confidently within its intended mission. These systems have to perform in environments that are unpredictable, with interference, jamming, and other real world performance limitations. Understanding how actual HW will perform in such environments is difficult if not impossible during the design and validation phases of new systems development, without costly facilities and complex measurement systems.This paper will explore and demonstrate solutions from Agilent Technologies that are built around a simulation core for the modeling of complex RADAR clutter environments, generation of actual real world waveforms that include potential design and environmental impairments, and analysis of systems performance with actual statistical metrics for sensitivity, selectivity, dynamic range, false alarm rate, detection rate, and estimation of range, velocity and angles. As an example in the presentation will be digital array radar (DAR) systems with Beamforming. The methodology for system performance evaluation also can be extended to Pulse Doppler, FMCW and wideband radar systems.


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Advanced Radar works in complex environment with complex channel condition, strong clutters, jamming, interference and noise. System Platform is needed for both design and verification

Proposed solution with the following Platform capability Integration of components for the system performance User-friendly UI for modeling using variety of languages such as

C++, Math Lang, MATLAB code Algorithm creation/verification environment for signal processing Link to test equipment to verify the implemented hardwareo Test sources include radar signal generation with RCS, Clutter, Jamming,

Doppler frequency offset o Measurements include waveforms, spectra, detection rate, false alarm rate

(FAR)o Estimation of speed, distance and angles for the detected target

Radar Design Challenge and Solution

Modern radar systems use more complex signal formats working in wide or ultra-wide bandwidths, and operating in different frequencies bands. They also use advanced digital signal processing techniques to disguise their operation and overcome strong clutter and jamming in their environment. Addressing this complexity requires the generation of realistic test signals and system-level scenarios that can be used to create and verify the radar signalprocessing algorithms. While dedicated hardware simulators and field testing are typically used to generate these test signals, both are costly, time consuming and apply later in the design process. This application note presents a lessexpensive option for generating test signals early in system development. This approach to system-level design and verification uses the Agilent SystemVue environment, a Radar Model Library and commercial off-the-shelf test equipmentfor generation of continual and pulse radar waveforms, for both algorithm and hardware verification.


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Platform for Simulation

Signal Sources T/R Modules Antenna

LFM NLFM Barker /Frank

Coded UWB Source FMCW

DAC, DUC DDS LNA DDC ADC Digital T/R

Radar Environment Signal Processing

Antenna Models Antenna Array Antenna Propagation

Target RCS Clutter (1D & 2D) Jammer Interference

Digital Pulse Compression Moving Target Indication (MTI) Moving Target Detection (MTD) Constant False Alarm Rate (CFAR) Digital Beamforming Space-Time Adaptive

Processing (STAP)

Measurements

Waveform Spectrum Sensitivity Selectivity Dynamic Range Detection Rate False Alarm Rate

Waveform Generation

RF Transmitter

MeasurementDisplay

Duplexer

(T/R units)

RF Receiver

Antenna

Digital Signal Processing

Target Environments

RCS, Clutter, Jamming

Existing Radar, DSP, Algorithm models are available to construct custom systems. Custom models using Math Lang, C++, HDL code as well as subnet structure can be created with user-friendly UI.In this way, different components created by different people can be integrated together and tested in the system level for the system performance.


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Platform for Component Test

Signal ProcessorHardware

ESG/PSG/MXG/PXB81180/N6030/M9330

PSA/MXA/VSA/PXALA, Scopes

Waveform Generation

RF Transmitter

MeasurementDisplay

Duplexer

(T/R units)

RF Receiver

Antenna


Target Environments

RCS, Clutter, Jamming

In this slide, we show how can we use the platform to connect to instruments and test hardware based on the created algorithm.In SV, we have a interface model (Sink) to connect ESG/PSG/MXG as well as wideband ARBs 81180/N6030/M9330A. In this way, software data from SV can be downloaded to instruments for hardware test data.SV also can connect signal analyzers or Logic Analyzers or Scopes to provide additional measurements for users needs.


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Agenda

Let us talk about Key Radar Models in the library


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Basic Source Models Pulse signal generator Linear FM pulse waveform (LFM) Nonlinear FM waveform (NLFM) Binary phase coded waveform (Barker) Poly phase coded waveform (Frank, P-Code, ZC)

RADAR

LFM

BB_SamplingRate=10e+6Hz [fs]FM_Offset=0 [[0]]

Bandwidth=5e+6Hz [BandWidth]PRI_Combination=1 [[1]]

PRI=1e-3s [PRI]Pulsewidth=10e-6s [PulseWidth]

R1 {RADAR_LFM@RADAR Models}

PRI

I

Q

Pulse width

spectrum

PRI: pulse repeat interval

Brief introduction for signal sources. Typical Radar signals can be generated by using different models. As an example, the linear FM pulse signal can be created based on the mathematical definition of the LFM signals.Key parameters have been listed in the model parameter table. Users just need to specify Pulse width, Pulse repetition interval (PRI), Bandwidth, FM Off set and Sampling Rate, the right waveform and spectrum will be generated properly as shown in the slide.


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Source Models

UWB Signal FMCW Signal

UWB signals including Pulse UWB, LFM UWB, Noise UWB and OFDM UWB

FMCW Signal

Ultra Wideband Radar signala and FM CW signals also can be generated by using Radar library models.


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Radar Cross Section (RCS)

Radar Cross Section (RCS) is a measure of the size of the target, as seen by the radar

Use statistical distribution to model RCS fluctuation Type: Const Value, Uniform PDF, Gaussian PDF, Rayleigh

PDF, LogNormal PDF, Exponential PDF, Weibull PDF, ChiSquared PDF, Gamma PDF, Beta PDF, F PDF, Binomial CDF, Poisson CDF

This model is for describing the Radar Target Cross Section with user-settable probability density function (pdf). The output values in one duration time do not change. A radar detects or tracks a target, and sometimes can classify it, only because there is an echo signal. The radar cross section or RCS is used to describe the echo of the target. The formal definition of radar cross section isdepends on electric-field strength of the incident wave impinging on the target, the electric-field strength of the scattered wave at the radar, the range from the radar to the target. Radar cross section fluctuates as a function of radar grazing angle, polarization and frequency. The RCS of simple bodies can be computed exactly by a solution of the wave equation in a coordinate system. Several statistical distribution is supplied in this model to model the RCS and output in port RCS.


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Target Return Model target echo received by radar antenna Including RCS, Doppler, delay, attenuation, propagation effects Fluctuant RCS types: Swirling 0, I, II, III, IV Echo: u(t 2R0/c) exp(j2(fc+fd)t) exp(-j4fcR0 /c) A k

u(t): Tx signal R0: target distance v: target radial velocity c: speed of light fc: carrier frequency Doppler frequency fd : 2 v fc / c k: free space propagation : RCS fluctuation A: attenuation besides

free space propagation

Target return model includes RCS, Doppler effect, Delay and Attenuation . Fluctuant RCS types: Swirling 0, , , , linear FM pulse signal generator.The target acts as a virtual antenna to reflect the electromagnetic waves sending from RADAR antenna to RADAR antenna. The echo in RADAR antenna is used to analyze the target. In this model, the input is the transmit signal in RADAR transmit antenna, the output is the echo signal from target in RADAR receive antenna.


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Clutters Clutter: echo from unwanted objects Use statistical approach to model clutter; combination of

Probability density functions (PDF) for clutter amplitude Rayleigh Log-Normal Weibull

Clutter power spectrum density (PSD) Gaussian Cauchy All Pole

Brief introduction for clutters.Since this is the system level simulation we use behavior models for describing the functionality. So, we focus on PDF and PSD and that are good enough to tell the system performance. We dont provide physical level model here. If the user really want, we still can provide special service for this purpose.


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Transmitter

Customizable Tx Sub-Network

Digital Up-Converter

DUC

Phase Noise

Filter Synthesis Non-Linearities

Radar transmitter front-endBaseband I/Q Digital IF IF RFComplex envelope representationOscillator, mixer, non-linear amplifier, filter


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Receiver

RF Gain w/ noise figure BPF DDC

Customizable Rx Sub-Network

Digital Up-Converter

Radar transmitter front-endBaseband I/Q Digital IF IF RFComplex envelope representationOscillator, mixer, non-linear amplifier, filter


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Pulse Compression

Filtering using DFT / IDFT Y(w) = S() H() y(t) = IDFT( Y() )

Windowing Y() = S() H() W() Control side-lobes Main lobe broadened

DFTDFTDFT IDFT

DFT

s(t)

windowW()

S()

reverse / conjugatex(t)

h(t) = x*(TM-t)

H()

y(t)Y()

Matched filter h(t) = x*(TM-t) s(t): received signal Output: y(t) = s(t) convolve h(t)

Radar range resolution depends on the bandwidth of the received signal.The bandwidth of a time-gated sinusoid is inversely proportional to the pulse duration. So short pulses are better for range resolution.Pulse compression allows us to use a reduced transmitter power and still achieve the desired range resolution.


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Pulse Doppler Processing

MTI then MTD MTI

Reduce clutter Indicate only the presence of moving

target MTD

Velocity Approaching / receding Multiple targets

The most common approach is taking an advantage of the Doppler effect. For a sequence of radar pulses the moving target will be at different distance from the radar and the phase of the radar return from the target will be different for successive pulses, while the returns from stationary clutter will arrive at the same phase shift.

MTI model is used to perform pulse canceller based Moving Target Indicator(MTI), in which a digital filter is designed to remove stationary return signal near the zero frequency.

A 2D signal processing must be performed. The first step in PD is to design a data bank to store received timed signals. We send in the received data to the data bank point by point from one column to another until the bank is full. Take a close look to each column, time interval is 1/Bandwidth. Each data point corresponding to return signal from different distance. Sampling interval between each column is the Pulse signal repetition interval. Each data point of data in the same row corresponding to return signal from the same distance with different timing. Doppler frequency can be extract from data in rows.


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Radar Detections

Mean law: x[n] = | c[n] | Square law: x[n] = | c[n] |2

Log square law: x[n] = ln( | c[n] |2 ) c[n]: complex-valued cell in the Range-Doppler matrix x[n] : real-valued cell in the Range-Doppler matrix after detector

If x[n] threshold T target detection

c[n] x[n]

Radar Detection is based on different rules.


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Constant False Alarm Detections False alarms are caused by: noise, clutter, interference, Neyman-Pearson criterion set constant PFA, then maximize PD

PFA: false alarm probability PD: detection probability

CFAR detection: Given detector type and PFA, compute a threshold T for each cell x[n] If x[n] function(T) target detection, return x[n];

otherwise no target, return 0 False detection happens, we want false alarm rate PFA

CA SOCA GOCA Clutter map

RADAR

CFAR

Beta=1.0Alpha=1.0Pf=1e-6

Threshold=PfDetector_Type=Square

GuardCell=4ReferenceCell=32

CellSize=4000 [PRI_Num]CFAR_Type=CA

CFAR

Since modern radar requires auto detection, advanced radar must use CFAR to control the false alarm rate. Otherwise the radar wont work. Essentially, the CFAR can be done in time, frequency or both domains.In the PD radar example, we only did it in frequency domain and use cell averaging CFAR.Instead, fixed detection threshold, we use the averaging amplitude value of neighbor cells as the threshold to avoid false alarm happened frequently.


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System performance Measurements

Basic measurements Waveform Spectrum Signal Noise Ratio

Advanced measurements Estimation of Distances and Speed Detection probabilityPd = Number of Successful detection /

Total number of Tests False Alarm probabilityPf = Number of False Error / Total Number

of Tests

We have implemented above models to do the measurements. If the user want more, we can come up with custom measurement models using combination of existing models


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Radar Range and Velocity Estimation

MTD filters are digital filters, so frequency response is periodic Nulls at multiples of PRF Hz Blind to targets at corresponding radial

velocity Could fix by raising PRF

Unambiguous range is inversely proportional to PRF

Tradeoff in PRF choice required

022 fcPRFPRFvblind ==

PRFcRua 2

=

We need to estimate Range and VelociyIn SV this capability has been established.


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Digital Array Radar (DAR) Why DAR?

Three main challenges to the radar designero Achieving high dynamic range (DR) to allow the use of a large PAo Achieving rapid volume searches while using an aperture with an inherently

narrow beam width o Maintaining robust wideband imaging performance, even in the presence of

strong land-based jammers. Digital array technology with digital Beamforming can be used to address

these three challenges. Basic structure

Waveform Generation

Transmitter

Measurement Display

Digital Array T/R Modules

Receiver

Array Antenna


When = , the channels are all time aligned for a signal from direction . Wm are beamformer weights. Gain in direction is wm. Less in otherdirections due to incoherent addition.


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Digital Phased Array

)(0 tx

)(1 tx

)(txM

)1( M

)(tsT

Miitstx Ti ,...2,1),()( ==

sincd

=

d 0w

1w

Mw

=

=1

0])1([)(

M

iii TiMtxwty

As an example, consider an Uniform Line Array Through signal processing as seen below, spatial filtering for interference can be

archived. Propagation can form a response pattern with higher sensitivity in desired directions.

Electronically scanned radars eliminate the mechanical challenges and errors associated with rotating and changing the elevation of a dish antenna. Array elements can create simultaneous beams to increase flexibility and capability

When = , the channels are all time aligned for a signal from direction . Wi are beamformer weights. Using Wi with each element allows the signal to point in any direction.Gain in direction is wm. Less in other directions due to incoherent addition.


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Digital Array T/R Modules DAR basic structure includes T/R module, phase shifters, beam forming network

and the antenna elements T/R modules are major components for both Passive electronically scanned

arrays (PESA) and Active electronically scanned arrays (AESA) The dominant architecture recently is the AESA that feeds each antenna element

with a lower power solid-state T/R module. T/R module is a key for DAR performance

The dominant architecture recently is the active electrically scanned array(AESA) where the final transmit power amplifier, receiver LNA,phase shift, and possibly a programmable attenuator are packaged into a transmit/receiver(T/R) module behind each element. The AESA has several advantages over the passive architecture. Transmit losses between the PA and the radiator and receive losses betweenthe radiator and the LNA are minimized. The AESA architecture provides additional beamforming flexibility since the amplitude and phase of each element can be dynamically controlled. This allows the aperture amplitude taper to be adjusted from pulse to pulse or from transmit to receive, whereas in a passive architecture the aperture amplitude taper is designed into the beamforming network of array.The solid-state amplifiers can be broader band than tube amplifiers, and the received signal can be digitized or optically modulated to support digital or optical beamforming. AESAs can operate even when a small percentage of the T/R modules fail, so they degrade more gracefully than the passive architecture.The digital Array T/R module is one of the most important module in the AESA.


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Beamforming Tx Beamformer

2D rectangular array transmission fixed Beamforming Support spatial steering in azimuth and elevation Support multiple simultaneous Tx beams Windowing to suppress sidelobes

10 20 30 40 50 60 70 80 90 100110120130140150160170

0 180

-90-80-70-60-50-40-30-20-10

010203040

-100

50

phi_deg

dB(S

igna

lPow

er_C

x)

Beam pattern in dB Beam pattern in polar

-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05-0.06 0.06

phi (0.000 to 3.142)

Sign

alPo

wer

_Cx

Beamforming is a signal processing technique used in radar antenna arrays for directional signal transmission or reception. This spatial selectivity is achieved by using adaptive or fixed receive/transmit beampatterns. The improvement compared with an omnidirectional reception/transmission is known as the receive/transmit gain (or loss).Beamforming can be used for both radio or radar waves. It has found numerous applications in radar, sonar, seismology, wireless communications, radio astronomy, speech, acoustics, and biomedicine. Adaptive beamforming is used to detect and estimate the signal-of-interest at the output of a sensor array by means of data-adaptive spatial filtering and interference rejection.

Beamforming takes advantage of interference to change the directionality of the array. When transmitting, a beamformer controls the phase and relative amplitude of the signal at each transmitter, in order to create a pattern of constructive and destructive interference in the wavefront. When receiving, information from different sensors is combined in such a way that the expected pattern of radiation is preferentially observed.

Beamforming refers to the coherent combination of data from multiple phase centers to provide selectivity in the angle of arrival, i.e. to formand steer an antenna beam.


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Rx Beamformer 2D rectangular array receiver fixed beamforming Support spatial steering in azimuth and elevation Support multiple simultaneous Rx beams Windowing to suppress sidelobes

10 20 30 40 50 60 70 80 90 100110120130140150160170

0 180

-140-130-120-110-100-90-80-70-60-50-40-30

-150

-20

phi_deg

dB(S

igna

lPow

er_C

x)

Beam pattern in dB Beam pattern in polar

Beamforming Cont

-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05-0.06 0.06

phi (0.000 to 3.142)

Sign

alPo

wer

_Cx

Beamforming is a signal processing technique used in radar antenna arrays for directional signal transmission or reception. This spatial selectivity is achieved by using adaptive or fixed receive/transmit beampatterns. The improvement compared with an omnidirectional reception/transmission is known as the receive/transmit gain (or loss).Beamforming can be used for both radio or radar waves. It has found numerous applications in radar, sonar, seismology, wireless communications, radio astronomy, speech, acoustics, and biomedicine. Adaptive beamforming is used to detect and estimate the signal-of-interest at the output of a sensor array by means of data-adaptive spatial filtering and interference rejection.

Beamforming takes advantage of interference to change the directionality of the array. When transmitting, a beamformer controls the phase and relative amplitude of the signal at each transmitter, in order to create a pattern of constructive and destructive interference in the wavefront. When receiving, information from different sensors is combined in such a way that the expected pattern of radiation is preferentially observed.

Beamforming refers to the coherent combination of data from multiple phase centers to provide selectivity in the angle of arrival, i.e. to formand steer an antenna beam.


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Spatial Target

Model 2D rectangular array target returns Support spatial steering in azimuth and elevation Including RCS, Doppler, delay, attenuation,

propagation effects Fluctuant RCS types: Swirling 0, I, II, III, IV

RCS, Doppler, delay, propagation

Spatial Target is to generate the target echo signal which is transmitted by all of the elements of the uniform linear array antenna or rectangular planar array antenna.The target is the point target.


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Ground ClutterThe clutter from a certain range patch

= =

=c aN

m

N

nnmnmmn fsax(t)

1 1,, ),(

amn, clutter in mth patch, nth rangeNc, number of azimuthal granularityNa, number of range ambiguities

The ground clutter contained in an iso-range ring centered at the radar will have a doppler-shift distribution due to the motion of the radar.The radar is in uniform motion neglecting for the moment any small elevation angle and curved Earth effects.

The antenna mode supports uniform linear array and 2-D rectangular planar array.

This model is a very simple spatial clutter model. The clutter model is a very complex simulation topic.


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Combining Spatial Target and Clutter

Figure illustrates the impact of uniform radar motion. In the absenceof platform motion (v = 0), the stationary clutter is concentrated along thezero-Doppler contour for all u . However, when v is not equal to 0, there is a linearrelationship between Doppler and sin u (or u ). As a result, the clutter energy is distributed along a line, or clutter ridge, asshown in Figure. Note that for the case illustrated, the antenna is aligned with direction of motion.


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Space-Time Adaptive Processing PD utilize the Doppler domain dof(degree of

freedom) only DBF utilize the angular domain dof STAP utilize both Doppler and angular dof 2D filtering Data aided optimal weight/filter coefficient

estimation In future, will support sub-optimal, high

computational efficiency algorithms

sRssRw Hopt 1

1

=

STAP

The need for joint space and time processing in either airborne or space-borne MTI radar arises from the inherent two-dimensional (2-D)nature of ground clutter.

The term STAP was first applied to multidimensional adaptive filtering of clutter and jamming in airborne MTI radars. Unlike ground-based(or ground-stationary) MTI radars, clutter returns manifest themselves as fully 2-D structures due to the motion-induced Doppler spreadingeffect. As a consequence, the traditional factored, or decoupled, approaches of beam forming followed by Doppler filtering (or vice versa) are not optimal.Instead, a better approach is to perform multidimensional filtering that accounts for angle-Doppler coupling.Space-time clutter is generally colored noise, that is, a nondiagonal covariance matrix. This is a good news/bad news situation: On the one hand, the fact that it has structure suggests that there may be an opportunity for separating the clutter subspace from the signalsubspace via space-time filtering.On the other hand, this can only be accomplished if we have an accurate model for the clutter structure.

In a high PRF (Doppler unambiguous) airborne MTI (AMTI) radar with good two-way antenna sidelobes, the targets of interest will be well removed (in Doppler) from the strongest main lobe clutter interference. This situation only requires a sidelobe notch that is well removed in angle-Doppler from the target main lobe.Since widening and deepening this notch width will have virtually no effect on the target main lobe response, the clutter model need not be very complex.

On the other handin stark contrast to the AMTI caseground MTI (GMTI) radars can often encounter slow targets that are very close (in angle-Doppler) to mainbeam clutter. In this case, a much higher-fidelity clutter model and multidimensional filtering scheme are required.

Since ground clutter can be exquisitely complexcomprised of all sorts of terrain, surface reflectivities, and internal motionthis modeling problem can be extremelychallenging. To make matters worse, this model must be implementable in real time. Indeed, much of the current STAP research is aimed at evermore refined and robust modeling (explicit or implicit) of ground clutter for just such applications.


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Application Examples Digital Array Radar (DAR) Pulse Doppler (PD) Radar Ultra Wideband (UWB) Radar Frequency Modulation Continue Waveform (FMCW) Radar


Agenda


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Example 1: DAR

System with DDC1

System with DDC2

As a template, this example can be used for DAR system components design and evaluation Key DAR components, working in complex environment, need to be modeled, simulated and evaluated to create proof-of-concept results for novel radar architectures. T/R modules constructing with different DDC, ADC, PA, System with phase/amplitude noise can be designed and evaluated using the template.

Digital T/R Module

DDC in the T/R Module

The DAR design can be used in the following areas:Algorithm creation for DAR including Beamforming/STAPWe provide a platform/testbed for designers to plug in their own algorithm and get system performanceDigital T/R module design and evaluationUsing our template to design and evaluate their components (DUC, DDC, ADC, DAC,) System integration for new/upgrade systemRadar companies upgrade their old product line from analog to digital or hybrid system. We provide a tool to integrate their components created by different SW/HW tools as a new and provide system performanceComponent testBy using ITS test their new components.


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Example 1: DAR As a template, this example can be used for DAR system components design and evaluation Beamforming is used. By selecting different Theta and Phi the received signal clutter ratio can be improved so that the the detection probability will be better.

Theta=100 Phi=50 Theta=50 Phi=50

Theta=100 Phi=50 Theta=100 Phi=50

Pd 80% 100%

Table: Detection Probability Vs Angles

Figure: Received waveforms with different angle setting for beamforming

The DAR design can be used in the following areas:Algorithm creation for DAR including Beamforming/STAPWe provide a platform/testbed for designers to plug in their own algorithm and get system performanceDigital T/R module design and evaluationUsing our template to design and evaluate their components (DUC, DDC, ADC, DAC,) System integration for new/upgrade systemRadar companies upgrade their old product line from analog to digital or hybrid system. We provide a tool to integrate their components created by different SW/HW tools as a new and provide system performanceComponent testBy using ITS test their new components.


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Example 2: PD Radar Ambiguity resolution As a template, this example can be used for radar algorithm creation and

testing PD Radar with single PRF only can estimate Radar parameters within limited

range and velocity. Low-PRF waveforms are ambiguous in range but ambiguous in Doppler,

medium-PRF waveforms are ambiguous in both range and Doppler, and high-PRF waveforms are ambiguous in range but unambiguous in Doppler.

An algorithm based on the Chinese Remainder Theorem is used to resolve the ambiguity problem.

In the algorithm two PRF are used to resolve the ambiguity

True Value Single PRF Multi PRFRange (KM) 300 45 299Velocity (m/s) 180 24 178

Ferrari, Berenguer, and Alengrin recently proposed an algorithm for velocity ambiguity resolution in coherent pulsed Doppler radar using multiple pulse repetition frequencies (PRF). In this algorithm, two step estimations for the Doppler frequency is used by choosing particular PRF values. The folded frequency is the fractional part of the Doppler frequency and is estimated by averaging the folded frequency estimates for each PRF. The ambiguity order is the integer part of the Doppler frequency and is estimated by using the quasi maximum likelihood criterion. The PRF are grouped into pairs and each pair PRF values are symmetry about 1. The folded frequency estimate for each pari is the circular mean of the two folded frequency estimates of the pair due to the symmetry property. In this paper, we propose a new algorithm based on the optimal choice of the PRF values, where the PRF values are also grouped into pairs. In each pair PRF values, one is given and the other is optimally chosen. The optimality is built upon the minimal sidelobes of the maximum likelihood criterion. Numerical simulations are presented to illustrate the improved performance.


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Example 2: PD Radar Ambiguity resolutionPut your algorithm model with M code or C++ code or HDL code here to evaluated.

A major classification of waveforms deals with ambiguity resolution in range and Doppler. Low-PRF waveforms are ambiguous in range but ambiguous in Doppler, medium-PRF waveforms are ambiguous in both range and Doppler, and high-PRF waveforms are ambiguous in range but unambiguous in Doppler. Previous techniques, the Chinese Remainder Theorem, the Hovanessian algorithm, and a clusteringalgorithm , have been developed for resolving the range and Doppler ambiguities of a single target for both medium-PRF and high-PRF waveforms.


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Example 3: UWB Radar This example can be used for different UWB radar signal generation ,

processing and measurement Ultra wide band

Bandwidth great than 0.5 GHz or Bandwidth great than 25% of central frequency

Four types: Pulse UWB, LFM UWB, Noise UWB, OFDM UWB Advantages

Through walls and obstacles capability for Geo- location and Positioning High accuracy for target detection Ease of signal generation and processing architectures Multipath immunity Low Cost

LFM Tx signal is a simple chirp with UWB properties.e.g. VHF radar image 20-90 MHz (but BW > 25% of fc)In use since long Problems: target modeling! too much information!For UWB noise radar, the question is how you model noise!Advantages:Frequency diversityImmunity to detection, jamming etc.Spectral efficiency (little cross-interference between 2 noise radars)Many proofs of concept availableFor pulse UWB signals, Non-sinusoidal waveformsFav. Shape: Gaussian waveforms Autocorrelation is Gaussian shape!FT is also Gaussian shape!Major advantages obtained from time domain analysisImpulse waveform: ~1nsDepth of pulse: ~ 30cmFiner resolution


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Agilent EEsof EDA, 2010

1. SV workspace for UWB Radar signal generation2. Transmitter signal can be generated for LFM, NLFM and coded signals3. Radar target return signals with RCS, Clutters, Jamming and interferences

also can be generated

LFM UWB Radar


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1. SV workspace for Pulse UWB Radar signal generation2. Transmitter signal can be generated for Sort Pulse Signal with Gaussian Window3. Radar target return signals with RCS, Clutters, Jamming and interferences


SV Workspace for Pulse UWB Radar


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1. SV workspace for Noise UWB Radar signal generation2. Transmitter signal can be generated for Noise UWB Radar3. Radar target return signals with RCS, Clutters, Jamming and interferences


SV Workspace for Noise UWB Radar


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1. FMCW Radar simulation example is provided for system evaluation2. Transmitter signal can be generated. Radar target return signals

with RCS, Clutters, Jamming and interferences also can be generated3. Signal processing subnet model is created to processing the FMCW signal4. Rage Estimation algorithm is created by using Math Lang model . Easy to use5. Simulation results show the algorithm working properly.

Example 4: FMCW Radar

Math Lang Model


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Agenda


40

Interface to Agilent Instruments UWB ARB

Link to UWB Signal Generator SignalDownloader_81180: can be

used for linking to 81180 Parameter settings listed in the

following table

In this slide, we show how can we use the platform to connect to instruments and test hardware based on the created algorithm.In SV, we have a interface model (Sink) to connect ESG/PSG/MXG. In this way, software data can be downloaded to instruments for hardware test data.SV also can connect signal analyzers or Logic Analyzers or Scopes to provide additional measurements for users needs.


41

Interface to Agilent InstrumentsLink to Signal Analyzer

VSA_89600_Source can be used for capturing data from MXA, PSA, PXA

Parameter settings listed in the following table. We assume you have known the following signal parameters

SamplingRate SignalRange

In SV examples, we will provide a VSA setup file (in this case test.set). User can use it as a template and modify it for your test case.

In this slide, we show how can we use the platform to connect to instruments and test hardware based on the created algorithm.In SV, we have a interface model (Sink) to connect ESG/PSG/MXG. In this way, software data can be downloaded to instruments for hardware test data.SV also can connect signal analyzers or Logic Analyzers or Scopes to provide additional measurements for users needs.


Integrated Test Solutions for Radar Test

Test Schedule Control

Instruments Configuration

Advanced Waveforms Generation

DUT Configuration

DUT Output Capture

Golden Reference Receiver

Advanced Measurements

Signal Generator Signal AnalyzerDUT

Integration Core Software

Test System Configuration using SystemVue as the Integration Core Software

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Generating Custom Waveforms: ESG/MXG/PSG Customized waveforms for new standards or proprietary formats. Complex waveforms such as mixed-mode, multi-modulation, waveform using specific framed data, or waveform with special modulation data.

Advanced MeasurementsMore generic receiver measurements such as detection rate, false alarm rate, sensitivity, selectivity can be added for regular signal analyzers. More key parameter estimations for Doppler frequency, velocity, region

Controlling and Integrating Test HW and SW Instruments Instrument control enables multiple instruments to evolve into a auto-test system. Providing Embedded Transmitter and Receiver Receiver component testing using instruments very often requires a golden transmitter, detector, receiver for a reference of measurement. During the developing period, the golden receiver is not ready yet, a software receiver in SystemVue can be embedded into the test system for receiver-troubleshooting and performance evaluation.


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Basic Waveform Generation Target Return signals

Radar Models

Clutter, noise, and Interference

BB Pattern Generator BB Arb. Waveform GenRF Signal Generator

DOWNLOADFROM

SystemVueSystemVue

Received Target Return Signal Target Return Signal with CluttersLFM Transmission Signal

As mentioned that we have a interface model (Sink) to connect ESG/PSG/MXG. SV can generate data and downloaded it to instruments for hardware test. In this slide, we show the details for using the platform to connected to instruments and test hardware based on the created algorithm.


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Advanced Measurements Receiver Test

Signal ProcessorHardware

ESG/PSG/MXG/PXBPSA/MXA/VSA/PXALA, Scope

As mentioned, SV also can connect to signal analyzers or Logic Analyzers or Scopes to provide additional measurements and expand instrument capability for users needs.

In this slide, we show the details about SV to signal analyzer link. Test signal from signal generator sends to DUT. The signal analyzer can capture the DUT output waveforms and send it to SV through SV to VSA link model. In SV, the waveform will be further processed using the Radar signal process functions and provide additional measurements.


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SystemVue/VSA Wideband PSG DUTInfiniium Scope 90K/M9392A M9330/

81180A

1. SV generates Radar Baseband I/Q data and download to 81180A/M9330/N60302. Wideband Radar test signal formed in 81180A and send to PSG wideband I,Q inputs3. RF wideband signals send to DUT to test the RF Radar components4. The output DUT is captured by using Infiniium scope and send back to the PC

for either direct analysis using VSA or further analysis using SV

UWB Signal Generation and Measurement

Generating Custom Waveforms: ESG/MXG/PSG Customized waveforms for new standards or proprietary formats. Complex waveforms such as mixed-mode, multi-modulation, waveform using specific framed data, or waveform with special modulation data.

Advanced MeasurementsMore generic receiver measurements such as detection rate, false alarm rate, sensitivity, selectivity can be added for regular signal analyzers. More key parameter estimations for Doppler frequency, velocity, region

Controlling and Integrating Test HW and SW Instruments Instrument control enables multiple instruments to evolve into a auto-test system. Providing Embedded Transmitter and Receiver Receiver component testing using instruments very often requires a golden transmitter, detector, receiver for a reference of measurement. During the developing period, the golden receiver is not ready yet, a software receiver in SystemVue can be embedded into the test system for receiver-troubleshooting and performance evaluation.


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UWB Radar Test Platform Setup

A platform is proposed for testing UWB Radar. In the platform, SystemVue Integreted Test Solution (ITS) is used with Agilent Wideband Signal Source (81180A/81190A, M9390), Vector Signal Source PSG, Wideband Vector Signal Analyzer and High Performance Scope (90000/90000X series) and form a UWB component tester in which different UWB signals can be generated in SystemVue, send to wideband arbitrary waveform generator as hardware component test signal. Also, different measurements are available in the platform. Examples show that this platform can be used not only for UWB transmitter but also for receiver troubleshooting and test purpose.

Copyright 2011 Agilent Technologies Agilent EEsof EDA, 2010

LFM UWB signal is generated using SV, downloaded to 81180 and PSG, then measured by using 90000X Infiniium Scope. Spectrum, Time waveform, Phase and Group delay are measured for the LFM UWB Signal with1 GHz BW and 1usc RPI

Test Results

47


LFM UWB signal is generated using SV, downloaded to 81180 and PSG, then measured by using 90000X Infiniium Scope. Spectrum, Time waveform, Phase and Group delay are measured for the LFM UWB Signal with2 GHz BW and 1usc RPI

Test Results

48


Pulse UWB signal is generated using SV, downloaded to 81180 and PSG, then measured by using 90000X Infiniium Scope. Spectrum, Time waveform, Phase and Group delay are measured for the LFM UWB Signal with0.5 GHz BW and 1usc RPI

Test Results

49


Wideband Signal Generation

Radar Received LFM signal with RCS generated using SV, N6030, PSG and 90000X Infiniium Scope.

50


Summary As seen from examples, SV Radar library can be used for PD, DAR,

UWB and FMCW radar design and testing SystemVue can increase competitive advantages to System Design

and HW Tests SystemVue has strong integration capability to integrate all SW models in C++,

MATLAB code, Math Lang, HDL code, together as a system. Without the integration, each model in different format and very hard to be verified in the system level for its performance

SV also can integrate all instruments together as a system test tool. Without the integration each HW instrument just provide single functionality. With SystemVue integration powerful system level solutions can be provided.

From functionality point of view Agilent Integrated Solutions can do more than any competitors in the following areas

Generate custom waveforms Advanced measurements Embedded reference transmitters and receivers

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References

1. I. Skolnik, Radar Handbook, 2nd ed. McGraw-Hill,Inc. 19902. D. Curtis Schleher, MTI and Pulse Doppler Radar, Artech

House, Inc. 19913. Dingqing Lu and Kong Yao "Importance Sampling Simulation

Techniques Applied to Estimating False Alarm Probabilities," Proc. IEEE ISCAS, 1989, pp.598-601

4. Dingqing Lu, Quasi-Analytical Method For Estimating low False Alarm Rate, EuRAD2010, 2010.



Additional Resources 1. Agilent SystemVue W1905 Radar Model Library,http://cp.literature.agilent.com/litweb/pdf/5990-6347EN.pdf

2. McClearnon, D. (2010), Defining a New Methodology for Radar SystemDesign, Microwave Product Digest,http://www.mpdigest.com/issue/Articles/2010/oct/Agilent/Default.asp

3. http://www.agilent.com/find/eesof-systemvue-radar-library

4. Ditore, Frank, Radar Glossary

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