volume | PIER Journals

Abstract

Synthetic Aperture Radar (SAR) can obtain a two-dimensional image of the observed scene. However, the resolution of conventional SAR imaging algorithm based on Matched Filter (MF) theory is limited by the transmitted signal bandwidth and the antenna length. Compressed sensing (CS) is a new approach of sparse signals recovered beyond the Nyquist sampling constraints. In this paper, a high resolution imaging method is presented for SAR sparse targets reconstruction based on CS theory. It shows that the image of sparse targets can be reconstructed by solving a convex optimization problem based on L1 norm minimization with only a small number of SAR echo samples. This indicates the sample size of SAR echo can be considerably reduced by CS method. Super-resolution property and point-localization ability are demonstrated using simulated data. Numerical results show the presented CS method outperforms the conventional SAR algorithm based on MF even though small sample size of SAR echo is used in this method.

1. Soumekh, M., Synthetic Aperture Radar Signal Processing with Matlab Algorithms, Wiley, New York, NY, 1999.

2. Curlander, J. C. and R. N. McDonough, Synthetic Aperture Radar: Systems and Signal Processing, John Wiley and Sons, 1991.

3. Cumming, I. G. and F. H. Wong, Digital Processing of Syethetic Aperture Radar Data: Algorithm and Implementatoin, Artech House Publishers, 2005.

4. Chan, Y. K. and V. C. Koo, "An introduction to synthetic aperture radar (SAR)," Progress In Electromagnetics Research B, Vol. 2, 27-60, 2008. Google Scholar

5. Moreira, A., J. Mittermayer, and R. Scheiber, "Extended chirp scaling algorithm for air- and spaceborne SAR data processing in stripmap and ScanSAR imaging modes," IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 5, 1123-1136, Sep. 1996. Google Scholar

6. Fang, L., X. Wang, and Y. Wang, "A modified SPECAN algo-rithm for synthetic aperture radar imaging," International Conference on Measuring Technology and Mechatronics Automation, Changsha, China, Mar. 2010. Google Scholar

7. Donoho, D., "Compressed sensing," IEEE Trans. Inf. Theory, Vol. 52, No. 4, 1289-1306, Apr. 2006. Google Scholar

8. Baraniuk, R., "Compressive sensing," IEEE Signal Processing, Vol. 24, No. 4, 118-121, Jul. 2007. Google Scholar

9. Candes, E. J. and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, 21-30, Mar. 2008. Google Scholar

10. Romberg, J., "Imaging via compressive sampling," IEEE Signal Processing, Vol. 25, No. 2, 14-20, Mar. 2008. Google Scholar

11. Bruckstein, A. M., D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse mofeling of signals and images," SIAM Review, Vol. 51, No. 1, 34-81, Feb. 2009. Google Scholar

12. Gurbuz, A. C., J. H. McClellan, and W. R. Scott, Jr., "Compressive sens-ing for GPR imaging," Proc. Asilomar Conf. Signals, Syst., Comput., 2223-2227, Nov. 2007. Google Scholar

13. Gurbuz, A. C., J. H. McClellan, and W. R. Scott, "A compressive sensing data acquisition and imaging method for stepped-frequency GPRs," IEEE Transation on Signal Processing, Vol. 57, No. 7, 2640-2650, Jul. 2009. Google Scholar

14. Huang, Q., L. Qu, B. Wu, and G. Fang, "UWB Throug-wall imaging based on compressive sensing," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 3, 1408-1415, 2010. Google Scholar

15. Bhattacharya, S., T. Blumensath, B. Mulgrew, and M. Davies, "Fastencoding of synthetic aperture radar raw data using compressedsensing," Proc. IEEE/SP Stat. Signal Process, 448-452, Madison, WI, Aug. 2007. Google Scholar

16. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," Proc. IEEE Radar Conf., 128-133, Boston, MA, Apr. 2007. Google Scholar

17. Herman, M. A. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Transactions on Signal Processing, Vol. 57, No. 6, 2275-2284, Jun. 2009. Google Scholar

18. Zhang, L., M. Xing, C. Qiu, et al. "Achieving higher resolution ISAR imaging with limited pulses via compressed sampling," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 3, 567-571, Jul. 2009. Google Scholar

19. Candes, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, Vol. 52, No. 2, 489-509, Feb. 2006. Google Scholar

20. Tropp, J. A., "Greed is good:algrorithmresults for sparse approximation," IEEE Trans. Inf. Theory, Vol. 50, No. 10, 2231-2242, Oct. 2004. Google Scholar

21. Needell, D. and R. Vershynin, "Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit," Found Comput Math, Vol. 9, No. 3, 317-334, Jun. 2009. Google Scholar

Dr. Shun-Jun Wei

Dr. Xiao-Ling Zhang

Dr. Jun Shi

Dr. Gao Xiang