基于马尔科夫链的单站SAR海面场景宽幅高分成像算法

doi:10.7527/S1000-6893.2016.0084

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基于马尔科夫链的单站SAR海面场景宽幅高分成像算法

倪嘉成¹, 张群¹, 顾福飞², 孙莉¹, 霍文俊¹

1. 空军工程大学信息与导航学院, 西安 710077;
2. 中国卫星海上测控部, 江阴 214430

收稿日期:2015-11-27 修回日期:2016-03-15 出版日期:2016-12-15 发布日期:2016-03-22
通讯作者: 张群,Tel.:029-84791751,E-mail:zhangqunnus@gmail.com E-mail:zhangqunnus@gmail.com
作者简介:倪嘉成,男,博士研究生。主要研究方向:SAR雷达信号处理,雷达成像。E-mail:littlenjc@sina.com;张群,男,博士,教授,博士生导师。主要研究方向:雷达信号与信息处理、雷达成像。Tel.:029-84791751,E-mail:zhangqunnus@gmail.com
基金资助:
国家自然科学基金（61501498）；陕西省自然科学基础研究计划（2015JM6306）；陕西省统筹创新工程-特色产业创新链（2015KTTSGY04-06）

Mono-static SAR HRWS imaging algorithm of sea surface based on Markov chain

NI Jiacheng¹, ZHANG Qun¹, GU Fufei², SUN Li¹, HUO Wenjun¹

1. School of Information and Navigation, Air Force Engineering University, Xi'an 710077, China;
2. China Satellite Maritime Tracking and Control Department, Jiangyin 214430, China

Received:2015-11-27 Revised:2016-03-15 Online:2016-12-15 Published:2016-03-22
Supported by:
National Natural Science Foundation of China (61501498); Nature Science Foundation Research Program of Shaanxi Province (2015JM6306); Coordinator Innovative Engineering Project of Shaanxi Province (2015KTTSGY04-06)

摘要/Abstract

摘要：

针对单站合成孔径雷达（SAR）实现海面场景高分辨率宽测绘带（HRWS）成像问题，结合海面目标相对整个场景的稀疏特性，提出了一种基于马尔科夫链的单站SAR宽幅高分成像算法。算法将宽幅的海面场景分为不同子测绘带，首先发射少量脉冲对各子测绘带进行距离向成像，利用距离向成像结果获取场景内感兴趣目标的数量信息。然后计算雷达波束指向的马尔科夫状态转移概率，并按此概率控制雷达对不同测绘带进行扫描。获得不同测绘带的稀疏子孔径后进行压缩感知成像。提出的算法可以在相同合成孔径时间内实现多个测绘带的宽幅高分成像，最后的仿真实验验证了所提算法的有效性。

关键词: SAR成像, 压缩感知, 距离像, 马尔科夫链, 迭代阈值算法

Abstract:

This paper focuses on the problem of solving high resolution wide swath (HRWS) SAR imaging of sea surface using mono-static SAR and proposes a compressed sensing imaging method based on Markov chain. This method is inspired by the fact that target of sea surface usually owns a sparse distribution. Firstly, the wide swath sea scene is divided into several sub scenes. Secondly, a few pulses were used to generate a range profile, and the range profile is used to obtain the target number in each sub scenes. After that the radar scanning probability can be calculated using the target number. The radar then scan each sub scene based on the scanning probability. Thirdly, an improved compressed sensing algorithm is utilized to reconstruct each sub scenes; the whole wide swath image is united by putting all the sub scenes together. The proposed method can get a wide swath image without reducing the resolution of the radar. Finally, the effectiveness of the proposed method is proved by the simulation results.

Key words: SAR imaging, compressed sensing, range profile, Markov chain, Iterative thresholding algorithm

中图分类号:

V243.2
TN957

倪嘉成, 张群, 顾福飞, 孙莉, 霍文俊. 基于马尔科夫链的单站SAR海面场景宽幅高分成像算法[J]. 航空学报, 2016, 37(12): 3793-3802.

NI Jiacheng, ZHANG Qun, GU Fufei, SUN Li, HUO Wenjun. Mono-static SAR HRWS imaging algorithm of sea surface based on Markov chain[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(12): 3793-3802.

参考文献

[1] 林月冠, 张冰尘, 田野, 等. 一种基于压缩感知的高分辨率宽测绘带合成孔径雷达成像算法[J]. 宇航学报, 2013, 34(1):106-112. LIN Y G, ZHANG B C, TIAN Y, et al. HRWS SAR imaging based on compressed sensing[J]. Journal of Astronautics, 2013, 34(1):106-112(in Chinese).
[2] WEN Q W, JING Y C. MIMO SAR using chirp diverse waveform for wide-swath remote sensing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4):3171-3185.
[3] WEN Q W. MIMO SAR imaging:Potential and challenges[J]. IEEE Transactions on Aerospace and Electronic Systems Magazine, 2013, 28(8):18-23.
[4] 武其松, 邢孟道, 刘保昌, 等. 面阵MIMO SAR大测绘带成像[J]. 电子学报, 2010, 38(4):817-824. WU Q S, XING M D, LIU B C, et al. Wide swath imaging with the plane array MIMO SAR system[J]. Acta Electronics Sinica, 2010, 38(4):817-824(in Chinese).
[5] 周伟, 刘永祥, 黎湘, 等. MIMO-SAR技术发展概况及应用浅析[J]. 雷达学报, 2014, 3(1):10-18. ZHOU W, LIU Y X, LI X, et al. Brief analysis on the development and application of multi-input multi-output synthetic aperture radar[J]. Journal of Radars, 2014, 3(1):10-18(in Chinese).
[6] JIE W, XIONG D L, CHI B D, et al. A novel scheme for ambiguous energy suppression in MIMO-SAR systems[J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2015, 12(2):344-348.
[7] SHUANG X Z, MENG D X, XIANG G X, et al. A robust imaging algorithm for squint mode multi-channel high-resolution and wide-swath SAR with hybrid baseline and fluctuant terrain[J]. IEEE Journal of Selected Topics Signal Processing, 2015, 9(99):1-16.
[8] MOORE R K, CLAASSEN J P, LIN Y H. Scanning spaceborne synthetic aperture radar with integrated radiometer[J]. IEEE Transactions on Aerospace and Electronic Systems, 1981, 17(3):410-420.
[9] GUARNIERI A M, PRATI C. ScanSAR focusing and interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4):1029-1038.
[10] 万莉莉, 左伟华. 高分辨率宽测绘带Scan SAR压缩感知成像算法研究[J]. 信号处理, 2013, 29(4):466-473. WAN L L, ZUO W H. Research on high resolution wide swath scan SAR imaging algorithm based on compressive sensing[J]. Journal of Signal Processing, 2013, 29(4):466-473(in Chinese).
[11] 井伟, 张磊, 邢孟道, 等. 聚束式SAR的宽场景成像算法[J]. 电子学报, 2009, 37(3):470-475. JING W, ZHANG L, XING M D, et al. Algorithm for wide-swath spotlight SAR data processing[J]. Acta Electronics Sinica, 2009, 37(3):470-475(in Chinese).
[12] 杨东, 廖桂生, 朱圣棋, 等. 宽测绘带SAR成像的一种侧摆模式及其压缩感知处理方法[J]. 电子与信息学报, 2012, 34(11):2594-2600. YANG D, LIAO G S, ZHU S Q, et al. A lateral swing mode for wide swath SAR imaging based on the compressive sensing approach[J]. Journal of Electronics & Information Technology, 2012, 34(11):2594-2600(in Chinese).
[13] BOFILL P, ZIBULEVSKY M. Underdetermined blind source separation using sparse representation[J]. Signal Processing, 2001, 81(11):2353-2362.
[14] YANG J, ZHANG H, YAMAGUCHI H. GOPCE based approach to ship detection[J]. IEEE Transactions on Geoscience and Remote Sensing Letters, 2012, 9(6):1089-1093.
[15] YIN J, YANG J, XIE J, et al. An improved generalized optimization of polarimetric contrast enhancement and its application to ship detection[J]. IEICE Transactions on Communications, 2013, E96-B(7):2005-2013.
[16] 曾庆勋. 基于势函数与压缩感知的欠定盲源分离及应用[D]. 沈阳:辽宁大学, 2014:19-21. ZENG Q X. Under-determined blind source separation and its applications based on potential function and compressive sensing[D]. Shenyang:Liaoning University, 2014:19-21(in Chinese).
[17] JIAN F, ZONG B X, ZHANG B C, et al. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1):352-363.
[18] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[19] WEI S J, ZHANG X L, SHI J, et al. Sparse reconstruction for SAR imaging based on compressed sensing[J]. Progress in Electromagnetics Research, 2010, 109(4):63-81.
[20] 保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京:电子工业出版社, 2005:165-173. BAO Z, XING M D, WANG T. Radar imaging technology[M]. Beijing:Publishing House of Electronics Industry, 2005:165-173(in Chinese).

E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于马尔科夫链的单站SAR海面场景宽幅高分成像算法

Mono-static SAR HRWS imaging algorithm of sea surface based on Markov chain

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