Synthetic Aperture Radar (SAR) Image Formation using Hardware System TMS320C6711 and TMS320C6713

1. Synthetic Aperture Radar (SAR) Image Formation using Hardware System TMS320C6711 and TMS320C6713 Abigail Fuentes Inerys Otero INEL 5326 Prof. Domingo Rodríguez

2. Outline • Objective • Synthetic Aperture Radar (SAR) Imaging Processing • Design Method  • SAR Imaging Formation Hardware Design • SAR Image Formation Testbed Environment • SAR Image formation on TMS320C6711 • SAR Image formation on TMS320C6713 • Image Formation Results • Conclusions • References

3. Objective • Implement an algorithm for synthetic aperture radar (SAR) image formation on the TMS320C6711 and TMS320C6713 digital signal processing (DSP) boards.

4. Synthetic aperture radar (SAR) imaging processing • Consists of forming an image of a landscape or terrain surface using active sensing. • An antenna transmits and receives a series of pulse signals reflected from an area of interest. • The antenna is placed on a moving platform • Aircraft • Satellite • Azimuth direction is defined to be in the same direction parallel to the antenna. • Range direction is perpendicular to the azimuth direction.

5. Synthetic aperture radar (SAR) imaging processing A = azimuth direction R = range direction

6. Synthetic aperture radar (SAR) imaging processing • Raw Data – The signals that are reflected from the surface area form a reflectivity pattern. A convolution operation is performed between the reflectivity pattern and the impulse response function that characterizes the image formation system. This operation produces a two-dimensional raw data. • Range compression – A matched filter is define in terms of a range reference function which takes in consideration the sampling rate, the duration of the transmitted signal and the frequency modulation rate of radar pulse. The convolution is performed between each line of SAR data and the filter.

7. Synthetic aperture radar (SAR) imaging processing • Corner turning – Performs the transpose of a given matrix. • Azimuth compression – The azimuth reference function is characterized by the duration in which the target is maintained illuminated by the antenna beam, the phase variation detected in the received signal, and the pulse repetition frequency. The convolution is performed between each line of data and the block reference functions.

8. Design Method 

9. Design Method Range compression Azimuth compression

10. SAR Imaging Formation Hardware Design

11. SAR Image Formation Testbed Environment

12. SAR Image formation on TMS320C6711

13. SAR Image formation on TMS320C6713

14. 128x128 Images obtained using TMS320C6711 Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

15. 128x128 Images obtained using TMS320C6713 Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

16. 128 x128 Images obtained previously by Ana RamÍrez using MATLAB Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

17. 256x256 Images obtained using TMS320C6713 Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

18. 256x256 Images obtained previously by Ana RamÍrez using MATLAB Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

19. 512x512 Images obtained using TMS320C6713 Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

20. 512x512 Images obtained previously by Ana RamÍrez using MATLAB Raw Data Data Compressed in Range Applying Corner Turning to Data Compressed in Range Direction Data Compressed in Azimuth

21. conclusions • For the TMS320C6711 DSP board, raw data of size 128x128 were processed, whereas for the TMS320C6713 DSP board, image formation for raw data of sizes 128x128, 256x256, and 512x512 was achieved. • For raw data of size 512x512, the images were formed with more details and could be appreciated better, in comparison with raw data of smaller sizes. • Results obtained from testbed were similar to those obtained in MATLAB by Ana Ramírez.

22. References • Ana Beatriz Ramirez Silva, “On Implementation Time-Frequency Representations on Hardware/Software Computational Structures for SAR Aplications”, University of Puerto Rico, Mayagüez Campus, June 2006 •  Cihan Erba, “SAR Raw Data Aspects and Focusing via High Precision Algorithms”, Istanbul Technical University Electronics and Communication Engineering Department Maslak, Istanbul, Turkey, IEEE 2003. • Natural Resources Canada, “SAR Image Formation”. • Peter T. Gough and David W. Hawkins, “Unified Framework for Modern Synthetic Aperture Imaging Algorithms”, Department of Electrical and Electronic Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand, Vol. 8, 343–358 (1997), Received 12 August 1996; revised 18 January 1997. • Bruce Walker, Grant Sander, Marty Thompson, Bryan Burns, Rick Fellerhoff, and Dale Dubbert, “A High-Resolution, Four-Band SAR Testbed with Real-Time Image Formation”, Sandia National Laboratories, Albuquerque, New Mexico. • European Space Agency, “Chapter 1: The ASAR User Guide”, available from World Wide Web: <http://envisat.esa.int/handbooks/asar/CNTR1.htm#eph.asar.ug>. • Amit Aggarwal and Erlend Hansen, “Radar in general”, The Connexions Project, Houston Texas, available from World Wide Web: http://cnx.org/content/m11718/latest/

23. QUESTIONS