Based on the observation that sparsity assumption is well satisfied in the synthetic aperture radar (SAR) imaging applications, there is increasing interest in utilizing compressive sensing (CS) in SAR imaging. However, there are still several problems which should be concerned in CS-based imaging approaches. Firstly, inevitable noise and clutter challenge the performance of CS algorithms. Secondly, the super-resolving ability of CS algorithms is not sufficiently exploited in most cases. Thirdly, nonideal characteristics of mutual coherence affect the performance of CS algorithms in complex scenes. In this paper, a novel CS imaging framework is proposed for the purpose of improving the imaging performance of stepped frequency SAR. Meanwhile, a super-resolving imaging algorithm is proposed based on the nonquadratic optimization technique. Simulated and rail SAR measured data are applied to demonstrate the effectiveness of the novel framework with the proposed super-resolving algorithm. Experimental results validate the superiority of this method over previous approaches in terms of robustness in low SNR, better super-resolving ability and improved imaging performance in complex scenes.
2. Zhang, M., Y. W. Zhao, H. Chen, and W.-Q. Jiang, "SAR imaging simulation for composite model of ship on dynamic ocean scene," Progress In Electromagnetics Research, Vol. 113, 395-412, 2011.
3. Sun, J., S. Mao, G. Wang, and W. Hong, "Polar format algorithm for spotlight bistatic SAR with arbitrary geometry configuration," Progress In Electromagnetics Research, Vol. 103, 323-338, 2010.
4. Mohammadpoor, M., et al., "A circular synthetic aperture eadar for on-the-ground object detection," Progress In Electromagnetics Research, Vol. 122, 269-292, 2012.
5. Koo, V. C., et al., "A new unmanned aerial vehicle synthetic aperture radar for environmental monitoring," Progress In Electromagnetics Research, Vol. 122, 245-268, 2012.
6. Lim, T. S., C.-S. Lim, and V. C. Koo, "Autofocus algorithm performance evaluations using an integrated SAR product simulator and processor," Progress In Electromagnetics Research B, Vol. 3, 315-329, 2008.
7. Storvold, R., E. Malnes, Y. Larsen, K. A. Hogda, and S. E. Hamran, "SAR remote sensing of snow parameters in Norwegian areas-current status and future perspective," Journal of Electromagnetic Waves and Applications, Vol. 20, No. 13, 1751-1759, 2006.
8. Li, Y., G. H. Lv, and X. Z. Liu, "Moving-target velocity estimation in a complex-valued SAR imagery," Progress In Electromagnetics Research, Vol. 136, 301-325, 2013.
9. Park, S.-H., J.-I. Park, and K.-T. Kim, "Motion compensation for squint mode spotlight SAR imaging using effcient 2D interpolation," Progress In Electromagnetics Research, Vol. 128, 503-518, 2012.
10. Liu, Y., Y. K. Deng, and R. Wang, "An extended inverse chirp-z transform algorithm to process high squint SAR data," Progress In Electromagnetics Research, Vol. 138, 555-569, 2013.
11. Wu, J., Z. Li, Y. Huang, Q. Liu, and J. Yang, "Processing one-stationary bistatic SAR data using inverse scaled fourier transform," Progress In Electromagnetics Research, Vol. 129, 143-159, 2012.
12. Yang, J. G., J. Thompson, X. T. Huang, T. Jin, and Z. M. Zhou, "Random-frequency SAR imaging based on compressed sensing," IEEE Trans. on Geoscience and Remote Sensing, Vol. 51, No. 2, 983-994, Feb. 2013.
13. Budillon, A., A. Evangelista, and G. Schirinzi, "Three-dimensional SAR focusing from multipass signals using compressive sampling," IEEE Trans. on Geoscience and Remote Sensing, Vol. 49, No. 1, 488-499, Jan. 2011.
14. Baraniuk, R. and P. Steeghs, Compressive radar imaging, Proc. IEEE 2007 Radar Conference, 128-133, Waltham, MA, Apr. 2007.
15. Gurbuz, A. C., J. H. McClellan, and W. R. Scott, Compressive sensing for GPR imaging, Proc. 41st Asilomar Conference on Signals, Systems Computers (ACSSC), 2223-2227, Nov. 2007.
16. Gurbuz, A. C., J. H. McClellan, and W. R. Scott, "Compressive sensing for subsurface imaging using ground penetrating radar," Signal Processing, Vol. 89, No. 10, 1959-1972, 2009.
17. Austin, C. D., E. Ertin, and R. L. Moses, "Sparse signal methods for 3-D radar imaging," IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 3, 408-423, Jun. 2011.
18. Varshney, K. R., M. Cetin, J. W. Fisher, and A. S. Willsky, "Sparse signal representation in structured dictionaries with application to synthetic aperture radar," IEEE Trans. on Signal Processing, Vol. 56, No. 8, 3548-3561, Aug. 2008.
19. Zhu, X. X. and R. Bamler, "Tomographic SAR inversion by l1-norm regularization --- The compressive sensing approach," IEEE Trans. on Geoscience and Remote Sensing, Vol. 56, No. 8, 3548-3561, Aug. 2008.
20. Patel, V. M., G. R. Easley, D. M. Healy, and R. Chellappa, "Compressed synthetic aperture radar," IEEE Journal of Selected Topics in Signal Processing, Vol. 4, No. 2, 244-254, Apr. 2010.
21. Xing, S. Q., Q. F. Liu, D. H. Dai, Y. Z. Li, and X. S. Wang, "Polarimetric SAR tomography using l21 mixed norm sparse reconstruction method ," Progress In Electromagnetics Research, Vol. 130, 105-130, 2012.
22. Zhang, L., M. D. Xing, C. W. Qiu, J. Li, J. L. Sheng, Y. C. Li, and Z. Bao, "Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing ," IEEE Trans. on Geoscience and Remote Sensing, Vol. 48, No. 10, 3824-3838, Oct. 2010.
23. Chen, S., D. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Journal on Scientific Computing, Vol. 20, No. 1, 33-61, 1999.
24. Tropp, J. and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. on Information Theory, Vol. 53, No. 12, 4655-4666, Dec. 2007.
25. Needell, D. and R. Vershynin, Greedy signal recovery and uncertainty principles, Proc. SPIE, Vol. 6814, J-1-J-12, Bellingham, WA, 2008.
26. Alonso, M. T., P. L. Dekker, and J. J. Mallorqui, "A novel strategy for radar imaging based on compressive sensing," IEEE Trans. on Geoscience and Remote Sensing, Vol. 48, No. 12, 3824-3838, Dec. 2010.
27. Needell, D. and J. A. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comp. Harmonic Anal., Vol. 26, 301-321, 2008.
28. Xing, S. Q., Y. Z. Li, D. H. Dai, and X. S. Wang, "3D reconstruction of manmade objects using polarimetric tomographic SAR," IEEE Trans. on Geoscience and Remote Sensing, 455-458, 2012.
29. Jouny, I., "Compressed sensing for UWB radar target signature reconstruction," IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education (DSP/SPE) Workshop, 714-719, 2009.
30. Herman, M. and T. Strohmer, Compressed sensing radar, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 1509-1512, 2008.
31. Herman, M. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Trans. on Signal Processing, Vol. 57, No. 6, 2275-2284, Jun. 2009.
32. Cetin, M. and W. C. Karl, "Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization," IEEE Trans. on Image Processing, Vol. 10, No. 4, 623-631, Apr. 2001.
33. Candes, E., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information ," IEEE Trans.on Information Theory, Vol. 52, No. 2, 489-509, Feb. 2006.
34. Donoho, D., M. Elad, and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. on Information Theory, Vol. 52, No. 1, 6-18, Jan. 2006.
35. Potter, L. C., E. Ertin, J. T. Parker, and M. Cetin, Sparsity and compressed sensing in radar imaging, Proceedings of the IEEE, Vol. 98, No. 6, 1006-1020, Jun. 2010.
36. Samadi, S., M. Cetin, and M. A. Masnadi-Shirazi, "Sparse representation-based synthetic aperture radar imaging," IET Radar Sonar & Navigation, Vol. 5, No. 2, 182-193, 2011.
37. Roy, R., A. Paulraj, and T. Kailath, "ESPRIT --- A subspace rotation approach to estimation of parameters of cisoids in noise," IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. 34, 1340-1342, Oct. 1986.
38. Roy, R. and T. Kailath, "ESPRIT --- Estimation of signal parameters via rotational invariance techniques," IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. 37, 984-995, Jul. 1989.
39. Blu, T., P.-L. Dragotti, M. Vetterli, P. Marziliano, and L. Coulot, "Sparse sampling of signal innovations," IEEE Signal Processing Magazine, Vol. 31, No. 40, Mar. 2008.
40. Xing, S. Q., D. H. Dai, X. S. Wang, and T. Wang, "Two-dimensional ESPRIT super-resolution feature extraction using fully polarized measurements and its performance analysis," Acta Electronica Sinica, Vol. 37, No. 12, 2681-2687, 2009.
41. Guo, D., H. Xu, and J. Li, "Extended wavenumber domain algorithm for highly squinted sliding spotlight SAR data processing," Progress In Electromagnetics Research, Vol. 114, 17-32, 2011.
42. Cafforio, C., C. Prati, and F. Rocca, "Full resolution focusing of SEASAT SAR images in the frequency-wave number domain," Proc. 8th EARSel Workshop, 336-355, May 1988.
43. Bamler, R., "A comparison of range-doppler and wavenumber domain SAR focusing algorithms ," IEEE Trans. on Geoscience and Remote Sensing, Vol. 30, No. 4, 706-713, Jul. 1992.
44. Jafarpour, S., W. Xu, B. Hassibi, and R. Calderbank, "Efficient and robust compressed sensing using optimized expander graphs," IEEE Trans. on Information Theory, Vol. 55, No. 9, 4299-4308, Sep. 2009.
45. Cetin, M., W. C. Karl, and D. A. Castanon, "Feature enhancement and ATR performance using nonquadratic optimization-based SAR imaging," IEEE Trans. on Aerospace and Electronic Systems, Vol. 39, No. 4, 1375-1395, Oct. 2003.