基于压缩感知的ISAR高分辨成像算法

doi:10.3969/j.issn.1000-436x.2013.09.018

摘要/Abstract

摘要：

针对 ISAR 在短孔径条件下存在的方位向分辨率低、易受噪声干扰等问题，基于压缩感知理论，提出了一种适用于短孔径时间模式下的基于压缩感知的ISAR方位向高分辨成像算法——PH-SL0算法。该算法首先构建部分随机化哈达玛矩阵作为量测矩阵，PH 矩阵具有重构精度高、重构需要量测个数少的优点；然后将运算速度快、重构精度高且稳健性好的平滑 0-范数法（SL0,smoothed L0-norm）推广应用到雷达复数域进行信号重构，实现 ISAR的横向高分辨成像；最后对在短 CPI条件下提出的PH-SL0算法的横向分辨率问题进行了理论分析。仿真和实测数据结果表明，所提算法具有更高的聚焦性能、分辨率以及较好的抗噪性能。

关键词: 压缩感知, 部分随机化哈达玛, 短孔径时间, 平滑0-范数法

Abstract:

According to the problem that conventional imaging algorithms have some unavoidable shortcomings such as low resolution of image and being fragile to the noise with short coherent processing interval (CPI),an improved ISAR imaging algorithm via CS method,namely,PH-SL0 algorithm,was proposed.In the proposed algorithm,as a kind of measure matrix,partial randomizer Hadamard matrix (PH) has many advantages such as high reconstruction precision and low dimension of measure matrix.Meanwhile,as a reconstruction algorithm,SL0 has many advantages such as demanding fewer measurements than existing methods,having higher reconstructed accuracy and better robust.Therefore,making use of the advantages of PH and SL0,and extending them to the field of radar,the azimuth imaging with short imaging data could be implemented.Finally,simulation results and experimental results of real data show that the algorithm has higher imaging resolution and better robust to noise.

Key words: compressed sensing, partial randomizer Hadamard matrix, short CPI, smooth L0-norm

李少东,杨军,马晓岩. 基于压缩感知的ISAR高分辨成像算法[J]. 通信学报, 2013, 34(9): 150-157.

Shao-dong LI,Jun YANG,Xiao-yan MA. High resolution ISAR imaging algorithm based on compressive sensing[J]. Journal on Communications, 2013, 34(9): 150-157.

图/表 8

图1

图2

图3

图4

图5

图6

图7

图8

参考文献 15

[16]	MOHIMANI HOSEIN G,BABAIE-ZADEH M . A fast approach for overcomplete sparse decomposition based on smoothed L0 norm[J]. IEEE Trans on Signal Processing, 2009,57(1): 289-301.
[17]	DONOHO . Compressed sensing[J]. IEEE Trans Inform Theory, 2006,52(4): 1289-1306.
[18]	保铮, 邢孟道, 王彤 . 雷达成像技术[M]. 北京: 电子工业出版社, 2006. BAO Z , XING M D , WANG T . Radar Imaging Technique[M]. Beijing: Electronic Industry Publishing HousePress, 2006.
[19]	雷强, 李宏伟, 张群 ,等. 基于压缩感知的ISAR 像目标旁瓣抑制新方法[J]. 弹箭与制导学报, 2011,2: 143-150. LEI Q , LI H W , ZHANG Q ,et al. A new way of suppressing ISAR imaging sidelobe based on compressed sensing[J]. Journal of Projectiles,Rockets,Missiles and Guidance, 2011,2: 143-150.
[1]	BI Z , LI J , LIU Z S . Super resolution SAR imaging via parametric spectral estimation methods[J]. IEEE Trans Aerosp Electron Syst, 1999,35(1): 267-281.
[2]	SUWA K , IWAMOTO M . A two-dimensional bandwidth extrapolation technique for polarimetric synthetic aperture radar im ges[J]. IEEE Trans Geosci Remote Sens, 2007,45(1): 45-54.
[3]	焦李成, 杨淑媛, 刘芳 ,等. 压缩感知回顾与展望[J]. 电子学报, 2011,39(7): 1651-1662. JIAO L C,YANG S Y , LIU F , et a l . Development and prospect of compressive sensing[J]. Acta Electronica Sinica, 2011,39(7): 1651-1662.
[4]	全英汇, 张磊, 刘亚波 ,等. 利用压缩感知的短孔径高分辨 ISAR 成像方法[J]. 西安电子科技大学学报, 2010,37(6): 1022-1026. QUAN Y H , ZHANG L , LIU Y B ,et al. Method for achieving high resolution ISAR imaging with short aperture data via compressed sensing[J]. Journal of Xidian University, 2010,37(6): 1022-1026.
[5]	李宁, 汪玲 . 基于统计压缩感知的短孔径 ISAR 成像方法研究[J]. 中国雷达, 2011,2: 24-28. LI N , WANG L . Study on short CPI ISAR imaging via statistics CS[J]. Chinese Radar, 2011,2: 24-28.
[6]	赵光辉, 张天键, 沈方芳 ,等. 低信噪比下稳健压缩感知成像[J]. 航空学报, 2011,33(3): 561-571. ZHAO G H , ZHANG T J , SHEN F F ,et al. Robust compressive sensing sar imaging in low SNR conditions[J]. Acta Aeronauticaet Astronautica Sinica, 2011,33(3): 561-571.
[7]	张龙, 张磊, 邢孟道 . 一种基于改进压缩感知的低信噪比 ISAR 高分辨成像方法[J]. 电子信息学报, 2011,32(9): 2263-2267. ZHANG L , ZHANG L , XING M D . A new method of high resol ion ISAR imaging under low SNR based on improved compressi sensing[J]. Acta Electronica Sinica, 2011,32(9): 2263-2267.
[8]	戴琼海, 付长军, 季向阳 . 压缩感知研究[J]. 计算机学报, 2011,34(3): 425-434. DAI Q H , FU C J , JI X Y . Research on compressed sensing[J]. Chinese Journal of Computers, 2011,34(3): 425-434.
[9]	CANDES E,TAO . Decoding by linear programming[J]. IEEE Trans on Information Theory, 2005,51(12): 4203-4215.
[10]	ZHANG L,XING M D , QIU C W , et a l . Achieving higher resolution ISAR imaging with limited pulses via compressed sampling[J]. IEEE Geosci Remote Sens Lett, 2009,6(3): 567-571.
[11]	ZHANG L , XING M D , QIU C W ,et al. Resolution enhancement for inverse synthetic aperture radar imaging under low SNR via improved compressive sensing[J]. IEEE Trans Geosci Remote Sens, 2010,48(10): 3824-3838.
[12]	李晓波 . 基于压缩感知的量测矩阵研究[D]. 北京:北京交通大学, 2010. LI X B . Resarch on Meaurement Matrix Based on Compressed Sensing[D]. Beijing:Beijing Jiaotong University, 2010.
[13]	DO T T , GAN L . Fast and efficient compressive sensing ing structurally random matrices[J]. IEEE Transactions on Signa Processing, 2012,60(1): 139-154.
[14]	RONALD A,DEVORE . Deterministic constructions of compressed sensing matrices[J]. Journal of Complexity, 2007,23(4-6): 918-925.
[15]	RICHARD B , MARK D , RONALD D ,et al. A simple proof of the restricted isometry property for random matrices[J]. Constructive Approximation, 2008,28(3): 253-263.