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.
"Sparse Reconstruction for SAR Imaging Based on Compressed Sensing," Progress In Electromagnetics Research,
Vol. 109, 63-81, 2010. doi:10.2528/PIER10080805
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.
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.
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.
8. Baraniuk, R., "Compressive sensing," IEEE Signal Processing, Vol. 24, No. 4, 118-121, Jul. 2007.
9. Candes, E. J. and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, 21-30, Mar. 2008.
10. Romberg, J., "Imaging via compressive sampling," IEEE Signal Processing, Vol. 25, No. 2, 14-20, Mar. 2008.
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.
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.
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.
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.
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.
16. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," Proc. IEEE Radar Conf., 128-133, Boston, MA, Apr. 2007.
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.
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.
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.
20. Tropp, J. A., "Greed is good:algrorithmresults for sparse approximation," IEEE Trans. Inf. Theory, Vol. 50, No. 10, 2231-2242, Oct. 2004.
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.