Sponsored By Abstract 1 Ritamar Siurano – Undergraduate Student Prof. Domingo Rodriguez – Advisor Abigail Fuentes – Graduate Student Prof. Ana B. Ramirez – Collaborator RASP Group, ECE Department, AIP Group - UPRM, ICPS Group - UIS University of Puerto Rico at Mayaguez Rapid Systems Prototyping Laboratory (RASP) This work presents the design of DSP support algorithms for synthetic aperture radar (SAR) image formation operations. Computational results are presented for fast Fourier transforms (FFTs), matrix corner turning operations and the convolution process based on FFTs. Correlation implementation of transmitted and received SAR signals are also presented in this work. Introduction 2 Synthetic aperture radar (SAR) image formation is a technique for obtaining images of the Earth’s surface through pulsed microwave transmitted and received signals. This system transmits a series of pulses at a fixed repetition rate and it collects the backscattered signals. Through signal processing techniques the transmitted and received signals are treated by a SAR image formation system to produce an image that is usually enhanced in the azimuth direction when compared with standard real (vs. synthetic) aperture images. The main benefit of using a SAR instead of a RAR is that the length of the antenna is significantly reduced to obtain a more detailed image. Methodology 3 The following procedure was used for the implementation of the algorithms: i) A TMS320C6713 DSP Starter Kit (DSK) was utilized as development platform; ii) The TMS320C6713 DSP(figure2) was configured to test the various FFT algorithms; iii) These FFT algorithms were used to develop the indirect convolution process, the correlation algorithm(figure3) and corner turning implementation; iv) Computational results were obtained in terms of number of cycles and execution times; v) Range and Azimuth compression algorithms were developed using MATLAB. Results 4 Conclusions 5 This work presents the results for implementation efforts of FFT and of corner turning algorithms on the TMS320C6713 DSP unit. For these algorithms, the execution times obtained on the DSP unit were faster using internal memory. It also validates correlation algorithm results from CCS, and presents the image formation algorithms using range and azimuth compression in MATLAB. References 6 [1] A. Ramirez, M. Rodriguez, D. Rodriguez, “TMS320C6713 User’s Guide, ”University of Puerto Rico Mayaguez Campus, Mayaguez, Puerto Rico, [2] R. Chassaing, Digital Signal Processing and Application with the C6713 and C6416 DSK, Wiley-Interscience, John Wiley & Sons, Inc., NY, DSP Implementation of SAR Support Algorithms Figure 2 (a) – TMS320C6713 Board Range Resolution Azimuth Resolution RADAR TRAJECTORY RADAR FOOTPRINT RADAR PULSE L swath Courtesy of RADARSAT r RAR SAR Figure 1 – SAR Imaging Infrastructure TI’s Complex FFT Function Table 1: Internal Memory (196KB) Table 2: External Memory (16MB) Blind Test Correlation Figure 4: MATLAB Figure 5: Code Composer Studio Corner Turning Operation Table 3: Corner Turning Execution Times *Clock Frequency 225MHz TI’s Complex FFT function C TI’s Complex FFT function Assembly Number of Points Average Number of Cycles Average Execution Time (s) Average Number of Cycles Average Execution Time (s) E E E E E E E E E E E E E E E E-04 TI’s Complex FFT function C TI’s Complex FFT function Assembly Number of Points Average Number of Cycles Average Execution Time (s) Average Number of Cycles Average Execution Time (s) E E E E E E E E E E E E E E E E-02 Corner Turning IRAM (196Kb) Corner Turning SDRAM (16Mb) Number of Points Average Number of Cycles Average Execution Time (s) Average Number of Cycles Average Execution Time (s) 32x E E-04 64x E E x E E x E x E x E-01 Figure 3 – FFT Based Correlation Algorithm Image Formation Results in MATLAB Figure 6 128X128 Image Range Compression Azimuth Compression Final Image Raw Data Figure 7 256X256 ImageFigure X1024 Image Range Compression Azimuth Compression Final Image Raw Data Range Compression Azimuth Compression Final Image Raw Data