2000 2"d International Conference on Microwave and Millimeter Wave Technology Proceedings

Radar MTI/MTD Implementation and Performance

Chang Chee Hang, Wong Char Ming, Liu Weixian and Jeffrey S Fu

School of Electrical and Electronic Engineering Nanyang Technological University

Nanyang Avenue, Singapore (639798) Tel: 65-790-5015 Fax: 65-791-2687

Email: ejeffu@ntu.edu.sg

Abstract: Moving Target Indication (MTI) and Moving Target Detection (MTD) processing were implemented on TI TMS320C6201 Evaluation Module (EVM) digital signal processor as part of a Radar receiver. Simulation was performed and Algorithm tested in MatLab and Borland C++, before the final implementation. Optimization techniques were applied in effort to improve the processing time.

I. INTRODUCTION

Radar serves to detect objects within the area of observation and to estimate their positional co-ordinates. The character-ristics of the echo provide information such as the range, angular location of the target, its track or trajectory if it is moving, and predict future location [l].

Processing system [6] using Signal Processing Workbench (SPW). Experiments were done on two moving targets with land clutter and white thermal noise taken into consideration. Table 1 shows a two targets' parameters that are simulated.

The objective is to design and implement a simple digital radar MTI and MTD. Digital Signal Processor (DSP) provides great flexibility while its speed is the major concern why DSP is preferred. The final realization is built on TMS32OC6201 Evaluation Module (EVM) from Texas Instruments [7]. Different optimization techniques were to be applied to improve the efficiency of the programs.

The initial design is analyzed with the simulation of a complete Radar Doppler System. After extracting necessary data from the simulation results, the MTI/MTD sub- system is designed with software in Matlab and Borland C++.

11. SIMULATION

Research and simulation were carried out with different parameters in a Radar Doppler

I Target I I SPWParameters I Target 1 I I "

Sampling Frequency (Hz) I 2.0e6 I 2.0e6 Pulse Repetition Fre uenc (Hz

Time Delay for Target 2.0e-4 .5e-4

Gain for Target (dR) Velocity of target (m/s) I -10 I 7.5 FW Frequency (GHz) I 10.0 I 10.0

Table 1 SPW design parameters of two targets.

111. DESIGN

The main concept of the MTI/MTD processing system [3] is bought to the design board from the Pulse processing system in the SPW. The flowchart in Fig 1 shows a simplified structure of the MTI and MTD of the radar processing system.

0-7003-5743-4/00/$10.00 @ 2000 IEEE 674

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Figure 1: Flowchart of MTI/MTD Processing

The simulated signals from SPW were converted as raw data shown in Fig 2A to be used by Matlab and Borland C++, which are the software programs that are used for the design.

The data, arranged into matrix, are then processed by the delay canceller where vectors of each sample containing information of the targets and other interference such as clutters are compared with the previous to get the required processed signal. In the delay canceller, as clutter are considered as stationary objects, the cancellation process will cancel them out, leaving only the moving targets’ signals.

The processed signals are then Fast Fourier transformed to convert the data into frequency domain [4]. The transformed matrix was multiplied with its conjugate to extract the absolute of the MTI signal for display as Fig 2B.

Beside indication of the targets, detection is the also been carried out, to find out about the range of the targets and also the direction of where the target is heading [ 2 ] .

imaginary - echo 1 (1) real - echo

Direction = arctan

CT Range = -metre ( m )

2

The results of both the design in Matlab and Borland C++ were verified with that of the SPW simulation plots and targets parameters.

From Fig 2C, positive amplitude detects the target is coming toward the radar while the negative means the opposite.

Pld(rTheSgnaAtterPuke Cmpssim Before MI Plocesang 200 I 1

I 0 2000 4000 MKX) 8ooo 106333 12000 14000 16000

-200 I Tme

2w , 1

Am Pll lr 1w de

0

-1 w 1

0 2000 4000 M)W 8000 I O W 0 12000 14000 16000 B00 I

Tme

A) Raw data input to Matlab and C++

B) MTI processed output

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x Id 15,

Target 1 Range (m)

Target 1 Direction

Target 2 Range (m)

Target 2 Direction

C) MTD nrocessed output

30,000

Away

22,500

Toward

Figure 2: Matlab and C results

For the range of the targets, the targets can be seen as:

x is the point of samples as from the plot. O h s is the time duration between each samples at sampling frequency of 2MHz. The result is multiplied with the half the speed of light to get the range values of the targets. Table 2 shows the results of the two programs as compared with that of the SPW simulated values. The MatLab’s results were obtained with higher amplitude as compared wi th the latter. By using Borland C++, further improvement was attempted to enhance the speed of processing with an average of 24 milliseconds.

Parameter Matlab 1 B::td I SPW I I I I

Table 2: Results of Matlab vs. C++ vs. SPW

IV. IMPLEMENTATION

Code Composer Studio Version 1.0 is the software that is used and it came bundled with the DSP TI EVM board [ 101.

The aim of the program is to make the processing fast enough to meet the speed requirement of signal processing which is crucial on the indication and detection of targets.

Several optimization methods for C codes [8] were used to improve the efficiency. For data extraction, a Probe Point within Code Composer Studio is used to read data from a file on the host into the input buffers location.

Compiler Intrinsics that map directly to inline EVM assembly instructions were also used throughout the whole coding for further improvement in efficiency.

The compiler schedules as many instructions as possible in parallel to maximize code efficiency. To eliminate memory dependencies [9], the following techniques are applied:

Use keyword const to indicate those objects that are not modified by a function.

Use -pm (program-level optimization) opt.ion that gives compiler global accesses to the entire program or module and allows compiler to be more aggressive in eliminating dependencies.

Use -mt option that allows compiler use assumptions to eliminate memory dependencies.

Use -ms ensure that redundant loops are not generated, thereby reducing code size.

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Table 3 shows the improvement with variou optimization techniques.

Float Int

Int &

S

32 Good 1 12082 32 Poor 4005 1 B) Imaginary part of the data being probed into 32 Good 95298 i-buf

Without With Use optimizatio optimizatio Keyword

n n Const

* 139,374.3 139,345.6 139,182.8

** 278.75 278.70 278.36

A) Real part of the data being probed into r-buf * AVE. no. of cycles for each output sample of 16

** Tota1;ime for 400-output sample-of size-16 (ms)

Table 3 Testing results for 6400 samples signal

With the appropriate change in the declaration type, an improved processing speed was obtained as shown in Table 4.

Double V Good 131750

Table 4 Performances of different data types used

For representation, the graphs of the various plots of the refined C code are plotted. From the plot, the direction of the target can be seen clearly as having positive swing meaning that the target is flying towards the radar, while the negative represents the opposite.

The range and direction definition is as the same as that of the earlier design represented by equation (1) and (3). C) Result of the MTI and MTD Processed signal

Figure 3: Code Composer Studio’s Display

The rest of the work carried out was on the observation of the radar signals received with changes and additions of other

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parameters such as additional targets, rain clutter, sea clutter etc. Our observations verified the accuracy and consistency our coding and implementation.

V. CONCLUSION

In this paper, the MTI and MTD processor was implemented with the TI EVM Digital Signal Processor (DSP). The flow begins from research on the principles, to the simulation with SPW, to the design with Matlab and Borland C++, provide the steps necessary to final implementation. With the simulated data used, the performances of all approaches were analyzed.

The later stages saw much of the task to make refinement and optimization to the algorithm. The final result shows that the speed of the processor with refined C codes as the most efficient.

Through integration of the MTI & MTD processor with the rest of the modules, a DSP- implemented radar receiver module can be built.

REFERENCES

[ 13 Nathanson, Fred E. “Radar design principles: signal processing and the environment”, New York: McGraw-Hill, 1991.

[2] Edde, Byron. “Radar: principles, technology, applications”, N.J.: Prentice Hall, c1993.

[3] Schleher, D. Curtis, “MTZ and pulsed doppler radar”, Boston : Artech House, c1991

[4] Winthrop W. Smith, Joanne M. Smith “Handbook of Real-Time Fast Fourier Transform”, New York : IEEE PRESS, c 1995

[5] Proakis, J.G., & Manolakis, D.G., 1996. “Digital Signal Processing: Principles,

Algorithms, and Applications (3rd ed. ”). Prentice Hall.

[6] Cadence Design Systems Inc.: “SPW Jump Start Training”. ALTA Group Rev. 5/95.

[7] Texas Instruments : “TMS320C6201/6701 Evaluation Module Technical Reference ”.

[SI Texas Instruments: 6‘TMS320C6201/6701 Evaluation Module User Guide”, SPRU269D December 1998

[9] Texas Instruments: “TMS320C62W67X Programmer’s Guide”, SPRU198B February 1998

[lo] Texas Instruments: “TMS32OC62X DSP Design Workshop (Student Guide)”, DSP6-Notes-2.0 November 1997

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