一种高分辨的稀疏孔径ISAR成像方法

doi:10.3969/j.issn.1001-2400.2010.03.010

Abstract

Abstract:

The conventional linear interpolation method will introduce the estimation error when the gapped data of ISAR is large. A high resolution imaging method based on compressed sensing for gapped data of ISAR is proposed in this paper. We first construct a sparse basis dictionary and linear measurement matrix. Then the convex optimization means as the base pursuit can be used to extract the scatter property and Doppler frequency information. By all the steps above, the image of ISAR is formed in the plane of range and Doppler. No interpolation is needed for the sparse aperture, and no sidelobes exit in the ISAR images. Simulation results and real sparse ISAR data validate the feasibility and superiority of the approach.

Key words: ISAR, linear interpolation, compressed sensing, convex optimization, Doppler frequency

LI Jun;XING Meng-dao;ZHANG Lei;WU Sun-jun. High resolution imaging method for the sparse aperture of ISAR[J].J4, 2010, 37(3): 441-446+453.

References

[1]Cuomo K M, Piou J E, Mayhan J T. Ultrawide-band Coherent Processing [J]. IEEE Trans on Antennas Propag, 1999, 47(6): 1094-1107.
[2]Potter L C, Arun K S. Energy Concentration in Band-limited Extrapolation [J]. IEEE Trans on Acoustics Speech and Signal Processing, 1989, 37(7): 1027-1041.
[3]Li H J, Farhat N, Shen Y. A New Iterative Algorithm for Extrapolation of Data Available in Multiple Restricted Regions with Application to Radar Imaging [J]. IEEE Trans on AP, 1987, 35(5): 581-588.
[4]Donoho D L. Compressed Sensing [J]. IEEE Trans on Info Theory, 2006, 52(4): 1289-1306.
[5]Zhu Chenwei. Stable Recovery of Sparse Signals Via Regularized Minimization [J]. IEEE Trans on Info Theory, 2008, 54(7): 3364-3367.
[6]Zhang Lei, Xing Mengdao, Qiu Chenwei. Achieving Higher Resolution ISAR Imaging with Limited Pulses Via Compressed Sampling [J]. IEEE GRS Letter, 2009, 6(3): 567-571.
[7]Rauhut H, Schnass K, Vandergheynst P. Compressed Sensing and Redundant Dictionaries [J]. IEEE Trans on Info Theory, 2008, 54(5): 2210-2219.
[8]Candes E. The Restricted Isometry Property and Its Implications for Compressed Sensing [J]. Comptes Rendus Mathematique, 2006, 246(9): 589-592.
[9]Candes E, Romberg J, Tao T. Stable Signal Recovery from Incomplete and Inaccurate Measurements [J]. Communications on Pure and Applied Mathematics, 2005, 59(8): 1207-1223.
[10]刘丹华, 石光明, 周佳社. 一种冗余字典下的信号稀疏分解新方法[J]. 西安电子科技大学学报, 2008, 35 (2): 228-232.
Liu Danhua, Shi Guangming, Zhou Jiashe. New Method for Singal Sparse Decomposition Over a Redundant Dictionary[J]. Journal of Xidian University, 2008, 35(2): 228-232.
[11]保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005: 230-276.
[12]Herman M, Strohmer T. High-Resolution Radar via Compressed Sensing [J]. IEEE Trans on Signal Processing, 2009, 57(6): 2275-2284.
[13]Candes E, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information [J]. IEEE Trans on Info Theory, 2006, 52(2): 489-509.

[1]	WANG Hongyan,QIU Helei,PEI Tengda. Visual tracking method using discriminant dictionary learning [J]. Journal of Xidian University, 2019, 46(4): 150-158.
[2]	ZHOU Xueqin,FENG Xiangchu,JING Mingli. Adaptive quasi-newton projection algorithm for sparse recovery [J]. Journal of Xidian University, 2019, 46(3): 14-19.
[3]	ZENG Chuangzhan,ZHU Weigang,JIA Xin. Sparse-aperture ISAR imaging algorithm [J]. Journal of Xidian University, 2019, 46(3): 123-129.
[4]	LV Zhiguo;LI Ying. Compressed sensing-based Bayesian channel estimation algorithm [J]. Journal of Xidian University, 2018, 45(2): 13-18+25.
[5]	HE Wangpeng;SUN Wei;SU Bo;YANG Yunyi;GUO Baolong. Sparse feature extraction technique and its applications to machinery fault diagnosis [J]. Journal of Xidian University, 2018, 45(2): 154-159.
[6]	YANG Ming;LI Xiang;YANG Hao;LIU Xin;CHEN Kunqi. Joint optimization of cooperative sensing and transmission in energy-efficiency cognitive radio [J]. Journal of Xidian University, 2017, 44(3): 101-107.
[7]	LIU Shuaiqi;WANG Buhong;LI Longjun;LI Xia;CAO Shuai. DOA estimation method for two-dimensional hybrid MIMO phased-array radar [J]. Journal of Xidian University, 2017, 44(3): 157-163.
[8]	ZHANG Jiancheng;SU Tao. Novel detection algorithm for high-speed maneuvering multi-target with narrowband radar [J]. Journal of Xidian University, 2017, 44(2): 20-26.
[9]	GAO Yuexin;LI Zhenyu;XING Mengdao;ZHANG Zijing. New ISAR imaging interval selection method for ship targets on the sea [J]. Journal of Xidian University, 2017, 44(2): 27-32.
[10]	WU Zhenhua;LIU Hongwei;ZHANG Lei;KOU Na. Degree of freedom evaluation in the radiation field for metamaterial aperture imaging radar [J]. Journal of Xidian University, 2016, 43(6): 39-44.
[11]	KONG Fanqiang;BIAN Chending;LI Yunsong;GUO Wenjun. Hyperspectral unmixing method based on the non-convex sparse and low-rank constraints [J]. Journal of Xidian University, 2016, 43(6): 116-121.
[12]	ZHANG Yuhong;XING Mengdao. Approach to joint translational and rotational phase auto-focusing [J]. Journal of Xidian University, 2016, 43(5): 31-35.
[13]	WANG Jue;WANG Jun;WU Yong;DENG Yaqi;LUO Zhen. Multistatic passive radar imaging algorithm for the gapped aperture [J]. Journal of Xidian University, 2016, 43(3): 25-30.
[14]	WEI Yibo;WANG Hui;LI Ning;YU Weidong. Two dimension migration compensation method for W-band ISAR imaging [J]. Journal of Xidian University, 2016, 43(3): 155-160.
[15]	WANG Feng;XIANG Xin;YI Kechu;XIONG Lei. Support driven recovery algorithm for non-convex compressed sensing [J]. Journal of Xidian University, 2016, 43(2): 1-5+28.

High resolution imaging method for the sparse aperture of ISAR

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10