volume | PIER Journals

Abstract

Due to the inaccuracies in radar's measurement, autofocus including range alignment and phase compensation is always essential in inverse synthetic aperture radar (ISAR) imagery. Compressed sensing (CS) based ISAR imagery suggests that the image of target can be reconstructed from much fewer random pulses. Because the number of pulses is inadequate and the pulse intervals are nonuniform, conventional phase compensating algorithms can't work in CS imaging. In this paper, an iterative algorithm is proposed to compensate the phase errors and reconstruct high-resolution focused image from limited pulses. In each iteration, the image of target is reconstructed by CS method, and then the estimation of phase errors is updated based on the reconstructed image. By cycling these steps, well-focused image can be obtained. The smoothed ℓ₀ algorithm is used to reconstruct the image, and the idea of minimum entropy optimization is used to estimate the phase errors. Besides, a method of extracting range bins in range profile based on amplitude information is proposed, which can reduce the computational complexity and improve the speed of convergence considerably. Both simulation and experiment results from real radar data demonstrate the effectiveness and feasibility of our method.

1. Ozdemir, C., Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms, John Wiley and Sons, INC., 2012.
doi:10.1002/9781118178072

2. Zhou, F., X. Bai, M. Xing, and Z. Bao, "Analysis of wide-angle radar imaging," IET Radar, Sonar and Navigation, Vol. 5, No. 4, 449-457, 2011.
doi:10.1049/iet-rsn.2010.0076 Google Scholar

3. Liu, Y., J. Zou, S. Xu, and Z. Chen, "Nonparametric rotational motion compensation technique for high-resolution isar imaging via golden section search," Progress In Electromagnetics Research M, Vol. 36, 67-76, 2014. Google Scholar

4. Chen, C.-C. and H. C. Andrews, "Target-motion-induced radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 16, No. 1, 2-14, 1980.
doi:10.1109/TAES.1980.308873 Google Scholar

5. Wahl, D. E., P. H. Eichel, D. C. Ghiglia, C. V. Jakowatz, and Jr., "Phase gradient autofocusa robust tool for high resolution sar phase correction," IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 3, 827-835, 1994.
doi:10.1109/7.303752 Google Scholar

6. Martorella, M., F. Berizzi, and B. Haywood, "Contrast maximisation based technique for 2-d isar autofocusing," Sonar and Navigation IEE Proceedings-Radar, Vol. 152, No. 4, 253-262, 2005.
doi:10.1049/ip-rsn:20045123 Google Scholar

7. Berizzi, F., M. Martorella, A. Cacciamano, and A. Capria, "A contrast-based algorithm for synthetic range-profile motion compensation," IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 10, 3053-3062, 2008.
doi:10.1109/TGRS.2008.2002576 Google Scholar

8. Xi, L., L. Guosui, and J. Ni, "Autofocusing of isar images based on entropy minimization," IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 4, 1240-1252, 1999.
doi:10.1109/7.805442 Google Scholar

9. Donoho, D. L., "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582 Google Scholar

10. Cand Es, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, 489-509, 2006.
doi:10.1109/TIT.2005.862083 Google Scholar

11. Herman, M. A. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Transactions on Signal Processing, Vol. 57, 2275-2284, 2009.
doi:10.1109/TSP.2009.2014277 Google Scholar

12. Ender, J., "On compressive sensing applied to radar," Signal Processing, Vol. 90, 1402-1414, 2010.
doi:10.1016/j.sigpro.2009.11.009 Google Scholar

13. Amin, M. G. and F. Ahmad, "Compressive sensing for through-the-wall radar imaging," Journal of Electronic Imaging, Vol. 22, 030901, 2013.
doi:10.1117/1.JEI.22.3.030901 Google Scholar

14. Li, D., X. Li, Y. Cheng, Y. Qin, and H. Wang, "Radar coincidence imaging in the presence of target-motion-induced error," Journal of Electronic Imaging, Vol. 23, 023014, 2014.
doi:10.1117/1.JEI.23.2.023014 Google Scholar

15. Daiyin, Z., Y. Xiang, and Z. Zhaoda, "Algorithms for compressed isar autofocusing," IEEE CIE International Conference on Radar, 533-536, IEEE, Chengdu, 2011. Google Scholar

16. Zhu, D., Y. Li, X. Yu, W. Zhang, and Z. Zhu, "Compressed isar autofocusing: Experimental results," 2012 IEEE Radar Conference (RADAR), 425-430, 2012. Google Scholar

17. Lin, Q., Z. Chen, Y. Zhang, and J. Lin, "Coherent phase compensation method based on direct if sampling in wideband radar," Progress In Electromagnetics Research, Vol. 136, 753-764, 2013.
doi:10.2528/PIER12122203 Google Scholar

18. Wang, J. and X. Liu, "Improved global range alignment for isar," IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 3, 1070-1075, 2007.
doi:10.1109/TAES.2007.4383594 Google Scholar

19. Zhu, D., L. Wang, Y. Yu, Q. Tao, and Z. Zhu, "Robust isar range alignment via minimizing the entropy of the average range profile," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 2, 204-208, 2009.
doi:10.1109/LGRS.2008.2010562 Google Scholar

20. Xiaohui, Q., H. W. C. Alice, and Y. S. Yam, "Fast minimum entpy phase compensation for isar imaging," Journal of Electronics and Information Technology, Vol. 26, No. 10, 1656-1661, 2004. Google Scholar

21. Zhang, L., M. Xing, C.-W. Qiu, J. Li, and Z. Bao, "Achieving higher resolution isar imaging with limited pulses via compressed sampling," IEEE Geoscience and Remote Sensing Letters, Vol. 6, 567-571, 2009.
doi:10.1109/LGRS.2009.2021584 Google Scholar

22. Tropp, J. A. and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, Vol. 53, 2007. Google Scholar

23. Chen, S. S., D. L. Donoho, and M. A. Saunders, "Key words. overcomplete signal representation, denoising, time-frequency analysis, time-scale," Society for Industrial and Applied Mathematics, Vol. 20, No. 1, 33-61, 1998. Google Scholar

24. Mohimani, G. H., M. Babaie-Zadeh, and C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed l0 norm," IEEE Transactions on Signal Processing, Vol. 57, 289-301, 2007.
doi:10.1109/TSP.2008.2007606 Google Scholar

25. Liu, J., S. Xu, X. Gao, and X. Li, "Compressive radar imaging methods based on fast smoothed l0 algorithm," 2012 International Workshop on Information and Electronics Engineering, Vol. 29, 2209-2213, Elsevier Ltd., 2012. Google Scholar

26. Samadi, S., M. Çetin, and M. A. Masnadi-Shirazi, "Sparse representation-based synthetic aperture radar imaging," IET Radar, Sonar & Navigation, Vol. 5, No. 2, 182-193, 2011.
doi:10.1049/iet-rsn.2009.0235 Google Scholar

27. Yang, J., X. Huang, J. Thompson, T. Jin, and Z. Zhou, "Compressed sensing radar imaging with compensation of observation position error," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, 4608-4620, 2014.
doi:10.1109/TGRS.2013.2283054 Google Scholar

Dr. Qingkai Hou

Dr. Lijie Fan

Dr. Shaoying Su

Dr. Zeng Ping Chen