In order to make full use of the joint sparse physical characteristics of the radar echo to improve imaging performance. A novel super resolution Inverse SAR (ISAR) imaging method based on distributed compressed sensing theory is proposed. Firstly, the joint sparse echo model of the random chirp frequency-stepped signal is built and the pulse compression processing of each sub-pulse is processed. Secondly, owing to different random patterns of each group, different measurement matrices are constructed in accordance with the random pattern of sub-pulse signal. Then the corresponding compressed sensing model of the echo is built and the supper resolution range profile is obtained via the distributed compressed sensing theory. Finally, the supper resolution inverse synthetic aperture radar image can be obtained by a fast compressed sensing reconstruction algorithm, which is used to achieve the high resolution reconstruction in azimuth direction based on the sparse features. Theoretical analysis and simulation results show that the proposed method has the characteristics of high reconstruction accuracy, low sampling rate and strong anti-noise performance.
吕明久,李少东,杨军,马晓岩. 基于随机调频步进信号的高分辨ISAR成像方法[J]. 电子与信息学报, 2016, 38(12): 3129-3136.
Lü Mingjiu, LI Shaodong, YANG Jun, MA Xiaoyan. High Resolution ISAR Imaging Method Based on Random Chirp Frequency-stepped Signal. JEIT, 2016, 38(12): 3129-3136.
DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[2]
JIU Bo, LIU Hongchao, Liu Hongwei, et al. Joint ISAR imaging and cross-range scaling method based on compressive sensing with adaptive dictionary[J]. IEEE Transactions on Antennas & Propagation, 2015, 63(5): 2112-2121. doi: 10.1109/TAP.2015.2409876.
TIAN Ye, ZHANG Bingchen, and HONG Wen. Deformation detection approach for repeat-pass InSAR based on distributed compressed sensing[J]. Journal of University of Chinese Academy of Sciences, 2016, 33(1): 107-114. doi: 10.7523/j.issn.2095-6134.2016.01.016.
LI Shaodong, CHEN Wenfeng, YANG Jun, et al. Study on the fast sparse recovery algorithm via multiple measurement vectors of arbitrary sparse structure[J]. Acta Electronica Sinica, 2015, 43(4): 708-715. doi: 10.3969/j.issn.0372-2112. 2015.04.012.
[5]
ZHANG Shunsheng, ZHANG Wei, ZONG Zhulin, et al. High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing[J]. IEEE Transactions on Antennas & Propagation, 2015, 63(5): 2098-2111. doi: 10.1109/TAP.2015.2408337.
[6]
ERTIN E. Frequency diverse waveforms for compressive radar sensing[C]. Waveform Diversity & Design Conference, Niagara Falls, Ontario, Canada, 2010: 216-219. doi: 10.1109/ WDD.2010.5592522.
[7]
SUGAVANAM N and ERTIN E. Recovery guarantees for multifrequency chirp waveforms in compressed radar sensing [OL]. http://120.52.73.76/arxiv.org/pdf/1508.07969v1,2015.
SUN Yanpeng, WANG Yilin, and QU Lele. Stepped- frequency GPR imaging algorithm based on Bayesian compressive sensing[J]. Journal of Shenyang Aerospace University, 2015, 32(5): 68-73. doi: 10.3969/j.issn.2095-1248. 2015.05.010.
SHAO Peng, XING Mengdao, LI Xueshi, et al. Squinted high resolution SAR based on the frequency synthetic bandwidth[J]. Journal of Xidian University, 2015, 42(4): 28-34. doi: 10.3969/j.issn.1001-2400.2015.02.005.
[10]
SEYFRIED D and SCHOEBEL J. Stepped-frequency radar signal processing[J]. Journal of Applied Geophysics, 2015, 112: 42-51.
WANG Hongxian, LIANG Yi, XING Mengdao, et al. ISAR imaging via sparse frequency-stepped chirp signal[J]. SCIENTIA SINICA Informationis, 2011, 41(12): 1529-1540. doi: 1007/s11432-011-4353-1.
[12]
ZHU Feng, ZHANG Qun, LUO Ying, et al. A novel cognitive ISAR imaging method with random stepped frequency chirp signal[J]. SCIECE CHINA Information Sciences, 2012, 55(8): 1910-1924. doi: 10.1007/s11432-012-4629-0.
[13]
ZHANG Lei, QIAO Zhijun, XING Mengdao, et al. High- resolution ISAR imaging with sparse stepped-frequency waveforms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4630-4651. doi: 10.1109/TGRS.2011. 2151865.
CHEN Yichang, ZHANG Qunshan, CHEN Xiaoping, et al. An imaging algorithm of sparse stepped frequency SAR based on multiple measurement vectors model[J]. Journal of Electronics & Information Technology, 2014, 36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.
WU Min, ZHANG Lei, XING Mengdao, et al. Full polarization super-resolution radar imaging algorithm based on distributed compressive sensing[J]. Chinese Journal of Radio Science, 2015, 30(1): 29-36. doi: 10.13443/j.cjors. 2014041101.
[17]
DUARTE M F, SARVOTHAM S, BARON D, et al. Distributed compressed sensing of jointly sparse signals[C]. Signals, Systems & Computers, Asilomar, CA, USA, 2005: 1537-1541. doi: 10.1109/ACSSC.2005.1600024.
[18]
BARON D, DUARTE M F, WAKIN M B, et al. Distributed compressive sensing[OL]. http://arxiv. orb/pdf/0901. 3403v1, 2014.
DING Lufei, GENG Fulu, and CHEN Jianchun. Radar Principles(5th)[M]. Beijing: Publishing House of Electronics Industry, 2014: 1-2.
[20]
SUNDMAN D. Greedy algorithms for distributed compressed sensing[D]. [Ph.D. dissertation], KTH Royal Institute of Technology, 2014: 99-116.
[21]
DUARTE M F and ELDAR Y C. Structured compressed sensing: from theory to applications[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4053-4083. doi: 10.1109/ TSP.2011.2161982.
[22]
DAVIES M E and ELDAR Y C. Rank awareness in joint sparse recovery[J]. IEEE Transactions on Information Theory, 2010, 58(2): 1135-1146. doi: 10.1109/TIT.2011.2173722.
ZHANG Lei. Study on high resolution SAR/ISAR imaging and error correction[D]. [Ph.D. dissertation], Xidian University, 2012.
[24]
HUANG Yajing, WANG Xuezhi, LI Xiangli, et al. Inverse synthetic aperture radar imaging using frame theory[J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5191-5200. doi: 10.1109/TSP.2012.2208107.
