volume | PIER Journals

Abstract

Having the imaging ability of the area in front of flight direction, forward-looking synthetic aperture radar (SAR) has become a hot topic in areas of SAR research. Nevertheless, constrained by limited azimuth aperture length,the imaging of forward-looking SAR suffers from poor azimuth resolution. Aiming at this problem, an enhanced forward-looking SAR imaging algorithm is proposed in this paper. This algorithm takes both super-resolving ability and computational burden into account. Firstly, an imaging framework is proposed to decrease the computational burden. Secondly, an iterative regularization implementation of compressive sensing (CS) is proposed to improve azimuth resolution. Finally, imaging experiments based on simulated data and Ku-band complex valued image data from the MiniSAR system demonstrate the effectiveness of the proposed algorithm.

1. Witte, F., "Forward looking radar,", US Patent 5182562, 1993. Google Scholar

2. Sutor, T., F. Witte, and A. Moreira, "A new sector imaging radar for enhanced vision-SIREV," Proceedings of SPIE, 39-47, July 1999. Google Scholar

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

4. Wang, J. and Z. L. Zong, "Forward-looking SAR imaging algorithm via compressive sensing," Radar Science and Technology, Vol. 10, No. 1, 27-31, 2012. Google Scholar

5. Xu, G., Q. Q. Chen, Y. X. Hou, Y. C. Li, and M. D. Xing, "Super-resolution imaging of forward-looking scan SAR," Journal of Xidian University, Vol. 39, No. 5, 101-108, 2012. Google Scholar

6. Soldovieri, F., G. Gennarelli, I. Catapano, D. Liao, and D. Dogaru, "Forward-looking radar imaging: A comparison of two data processing strategies," IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 10, No. 2, 562-571, 2017. Google Scholar

7. Chen, Q. and R. L. Yang, "Research of chirp scaling imaging algorithm for air-borne forward-looking SAR," Journal of Electronics & Information Technology, Vol. 30, No. 1, 228-232, 2008. Google Scholar

8. Ren, X. Z. and R. L. Yang, "Study on three-dimensional imaging algorithm for airborne forward-looking SAR," Journal of Electronics & Information Technology, Vol. 32, No. 6, 1361-1365, 2010. Google Scholar

9. Ren, X. Z., J. T. Sun, and R. L. Yang, "A new three-dimensional imaging algorithm for airborne forward-looking SAR," IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 1, 153-157, 2011. Google Scholar

10. Ren, X. Z., L. L. Tan, and R. L. Yang, "Research of three-dimensional imaging processing for airborne forward-looking SAR," Proceedings of IET International Radar Conference, 1-4, April 2009. Google Scholar

11. Pang, B., D. H. Dai, S. Q. Xing, and X. S. Wang, "Development and perspective of forward-looking imaging technique," Systems Engineering and Electronics, Vol. 35, No. 11, 40-47, 2013. Google Scholar

12. 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 Trans. on Geoscience and Remote Sensing, Vol. 34, No. 5, 1123-1136, 1996. Google Scholar

13. Pang, B., S. Q. Xing, D. H. Dai, Y. Z. Li, and X. S. Wang, "Research on forward-looking synthetic aperture radar imaging algorithm of high velocity platform," Proceedings of IET International Radar Conference, 1-6, April 2013. Google Scholar

14. Pang, B., X. S. Wang, D. H. Dai, S. Q. Xing, and Y. Z. Li, "Imaging algorithm of high velocity forward-looking SAR based on digital beam sharpening," Chinese Journal of Radio Science, Vol. 29, No. 1, 1-8, 2014. Google Scholar

15. Yuan, Y., S. Chen, S. N. Zhang, and H. C. Zhao, "A chirp scaling algorithm for forward-looking linear-array SAR with constant acceleration," IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 1, 88-91, 2018. Google Scholar

16. Mei, H. W., Z. Q. Meng, M. Q. Liu, Y. C. Li, Y. H. Quan, S. Q. Zhu, and M. D. Xing, "Thorough understanding property of bistatic forward-looking high-speed maneuvering platform SAR," IEEE Trans. on Aerospace & Electronic Systems, Vol. 53, No. 4, 1826-1845, 2017. Google Scholar

17. Pu, W., J. J. Wu, Y. L. Huang, W. C. Li, Z. C. Sun, J. Y. Yang, and H. G. Yang, "Motion errors and compensation for bistatic forward-looking SAR with cubic-order processing," IEEE Trans. on Geoscience and Remote Sensing, Vol. 54, No. 12, 6940-6957, 2016. Google Scholar

18. Meng, Z. Q., Y. C. Li, M. D. Xing, and Z. Bao, "Property analysis of bistatic forward-looking SAR with arbitrary geometry," Systems Engineering and Electronics, Vol. 27, No. 1, 111-127, 2016. Google Scholar

19. Xia, J., X. F. Lu, and W. D. Chen, "Multi-channel deconvolution for forward-looking phase array radar imaging," Remote Sensing, Vol. 9, No. 7, 703-728, 2017. Google Scholar

20. Zhang, Y., Y. C. Zhang, Y. L. Huang, and Y. L. Yang, "A sparse Bayesian approach for forward-looking superresolution radar imaging," Sensors, Vol. 17, No. 6, 1353-1368, 2017. Google Scholar

21. Zhang, L., Z. J. Qiao, M. D. Xing, Y. C. Li, and Z. Bao, "High-resolution ISAR imaging with sparse stepped-frequency waveforms," IEEE Trans. on Geoscience and Remote Sensing, Vol. 49, No. 11, 4630-4651, 2011. Google Scholar

22. 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, 2011. Google Scholar

23. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," Proceedings of IEEE Radar Conference, 128-133, April 2007. Google Scholar

24. Austi, 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, 2011. Google Scholar

25. 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, 2008. Google Scholar

26. Gurbuz, A. C., J. H. McClellan, and W. R. Scott, "Compressive sensing for GPR imaging," Proceedings of 41st Asilomar Conference on Signals, Systems Computers, 2223-2227, November 4–7, 2007. Google Scholar

27. 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. Google Scholar

28. 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. 48, No. 10, 3839-3846, 2010. Google Scholar

29. 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, 2010. Google Scholar

30. Cumming, I. G. and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation, Artech House, 2005.

31. 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, 2001. Google Scholar

32. Potter, L. C., E. Ertin, J. T. Parker, and M. Cetin, "Sparsity and compressed sensing in radar imaging," Proc. of IEEE, Vol. 98, No. 6, 1006-1020, 2010. Google Scholar

33. 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. Google Scholar

34. Xing, S., D. Dai, Y. Li, and X. Wang, "Polarimetric SAR tomography using L_2,1 mixed norm sparse reconstruction method," Progress In Electromagnetics Research, Vol. 130, 105-130, 2012. Google Scholar

35. Magnus, J. R. and H. Neudecker, Matrix Differential Calculus with Applications in Statistics and Econometrics, John Wiley & Sons, 2007.

36. Sun, D., S. Q. Xing, Y. Z. Li, and D. H. Dai, "Adaptive parameter selection of SAR sparse imaging model," Journal of Remote Sensing, Vol. 21, No. 4, 579-587, 2017. Google Scholar

37. Austin, C. D., R. L. Moses, J. N. Ash, and E. Ertin, "On the relation between sparse reconstruction and parameter estimation with model order selection," IEEE Journal of Selected Topics in Signal Processing , Vol. 4, No. 3, 560-570, 2010. Google Scholar

38. Zhang, Y., G. X. Zhou, J. Jin, Q. B. Zhao, X. Y. Wang, and A. Cichocki, "Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface," International Journal of Neural Systems, Vol. 24, No. 1, 1450003, 2014. Google Scholar

39. Pang, B., D. H. Dai, S. Q. Xing, Y. Z. Li, and X. S. Wang, "Imaging enhancement of stepped frequency radar using the sparse reconstruction technique," Progress In Electromagnetics Research, Vol. 140, 63-89, 2013. Google Scholar

40. 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, 2013. Google Scholar

41. 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, 2003. Google Scholar

42. Guo, B., D. Vu, L. Z. Xu, M. Xue, and J. Li, "Ground moving target indication via multichannel airborne SAR," IEEE Trans. on Geoscience and Remote Sensing, Vol. 49, No. 10, 3753-3764, 2011. Google Scholar

43. Stoica, P., J. Li, and H. He, "Spectral analysis of non-uniformly sampled data: A new approach versus the periodogram," IEEE Trans. on Signal Processing, Vol. 57, No. 3, 843-858, 2009. Google Scholar

Dr. Bo Pang

Dr. Hao Wu

Dr. Shiqi Xing

Dr. Dahai Dai

Dr. Yongzhen Li

Dr. Xuesong Wang