An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit

WANG Wei; ZHANG Bin; LI Xin

doi:10.11999/JEIT151453

Volume 38 Issue 10

Oct. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(10): 2415-2422

WANG Wei, ZHANG Bin, LI Xin. An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2415-2422. doi: 10.11999/JEIT151453

Citation:

WANG Wei, ZHANG Bin, LI Xin. An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2415-2422. doi: 10.11999/JEIT151453

WANG Wei, ZHANG Bin, LI Xin. An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2415-2422. doi: 10.11999/JEIT151453

Citation:

WANG Wei, ZHANG Bin, LI Xin. An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2415-2422. doi: 10.11999/JEIT151453

PDF( 705 KB)

An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit

doi: 10.11999/JEIT151453

Funds:

The National Natural Science Foundation of China (61571148), China Postdoctoral Special Funding (2015T80328), China Postdoctoral Science Foundation (2014M550182), Heilongjiang Province Postdoctoral Special Fund (LBH-TZ0410), Innovation of Science, Technology Talents in Harbin (2013RFXXJ016)

Received Date: 2015-12-22
Rev Recd Date: 2016-06-17
Publish Date: 2016-10-19

Abstract

Abstract

MIMO radar is an emerging radar system that has significant potential. MIMO radar can provide high resolution and real-time imaging solution. Because of the sparsity of the observation zone, the task of MIMO radar imaging can be formulated as a problem of sparse signal recovery based on Compressed Sensing (CS). In MIMO radar imaging application based on CS, existing greedy algorithms, such as the Orthogonal Matching Pursuit (OMP) algorithm and the Subspace Pursuit (SP) algorithm, suffer from artifacts and low-resolution, respectively. To deal with the drawback of existing greedy algorithms, a Hybrid Matching Pursuit (HMP) algorithm is proposed to combine the strengths of OMP and SP. By using of the orthogonality among selected basis-signals and the backtracking strategy for basis-signal reevaluation, the HMP algorithm can reconstruct high-resolution radar image with no artifacts. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm.
- MIMO radar,
- Compressive sensing,
- Sparse imaging,
- Greedy algorithm

FullText(HTML)

References(34)

References

FISHLER E, HAIMOVICH A, BLUM R, et al. MIMO radar: an idea whose time has come[C]. IEEE Radar Conference,　Philadelphia, PA, USA, 2004: 71-78.

刘涛. MIMO雷达技术及其应用研究[J]. 无线互联科技, 2015, 6(12): 136-137. doi: 10.3969/j.issn.1672-6944.2015.12.064.

LIU Tao. Research on MIMO radar technology and its application[J]. Wireless Internet Technology, 2015, 6(12): 136-137. doi: 10.3969/j.issn.1672-6944.2015.12.064.

BLISS D W and FORSYTHE K W. MIMO radar medical imaging: Self-interference mitigation for breast tumor detection[C]. The 40th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2006: 1558-1562. doi: 10.1109/ACSSC.2006.355020.

王伟, 马跃华, 王咸鹏. 一种高运算效率的MIMO雷达BP成像算法[J]. 系统工程与电子技术, 2013, 35(10): 2080-2085.

WANG Wei, MA Yuehua, and WANG Xianpeng. High computation effciency BP imaging algorithm for MIMO radar[J]. Systems Engineering and Electronics, 2013, 35(10): 2080-2085.

ZHUGE X D, YAROVOY A G, SAVELYEV T, et al. Modified Kirchhoff migration for UWB MIMO array-based radar imaging[J]. IEEE Transactions on Geoscience and Remote Sensing,　2010, 48(6): 2692-2703. doi: 10.1109/TGRS.2010. 2040747.

OGIWARA S and YAMAKOSHI Y. MIMO radar system for respiratory monitoring using Tx and Rx modulation with M-sequence codes[J]. IEICE Transactions on Communications, 2010, 93(9): 2416-2423. doi: 10.1587/ transcom.E93.B.2416.

BARANIUK R and STEEGHS P. Compressive radar imaging[C]. 2007 IEEE Radar Conference, Boston, MA, USA, 2007: 128-133. doi: 10.1109/RADAR.2007.374203.

杨杰, 廖桂生, 李军. 基于波形选择的MIMO雷达三维稀疏成像与角度误差校正方法[J]. 电子与信息学报. 2014, 36(2): 428-434. doi: 10.3724/SP.J.1146.2013.00500.

YANG Jie, LIAO Guisheng, and LI Jun. Three dimensional MIMO radar imaging using sparse model based on Waveform Selection and Calibration Method in the Presence of Angle Imperfections[J]. Journal of Electronics Information Technology, 2014, 36(2): 428-434. doi: 10.3724/SP.J.1146. 2013.00500.

丁丽. MIMO雷达稀疏成像的失配问题研究[D]. [博士论文], 中国科学技术大学, 2014.

DING Li. Ressearch on observation matrix mismatch for MIMO radar sparse imaging[D]. [Ph.D. dissertation], University of Science and Technology of China, 2014.

WILLIAM R, PETRE S, LI J, et al. Iterative adaptive approaches to MIMO radar imaing[J]. IEEE Journal of Selected Thopics in Signal Processing, 2010, 4(1): 5-20. doi: 10.1109/JSTSP.2009.2038964.

TAN X, ROVERTS W, LI J, et al. Sparse learning via iterative minimization with application to MIMO radar imaging[J]. IEEE　Transactions on Signal Processing, 2010, 59(3): 1088-1101. doi: 10.1109/TSP.2010.2096218.

王伟, 马跃华, 郝燕玲. 基于MAPC-RISR的MIMO雷达距离-角度二位超分辨率成像算法[J]. 中国科学: 信息科学, 2015, 45(3): 372-384. doi: 10.1360/N112014-00044.

WANG Wei, MA Yuehua, and HAO Yanling. High-resolution MIMO radar range-angle 2D imaging algorithm based on MAPC-RISR[J]. Scientia Sinica Informationis, 2015, 45(3): 372-384. doi: 10.1360/N112014-00044.

HIGGINS T, BLUNT S D, SHACKELFORD A K, et al. Space-range adaptive processing for waveform-diverse radar imaging[C]. IEEE Radar Conference, Arlington, VA, USA, 2010: 321-326. doi: 10.1109/RADAR.2010.5494604.

HUANG Q, QU L, WU B, et al. UWB through-wall imaging based on compressive sensing[J]. IEEE Traqnsactions on Geoscience and Reomote Sensing,　2010, 48(3): 1408-1415. doi: 10.1109/TGRS.2009.2030321.

TANG V H, Bouzerdoum A, Phung S L, et al. Enhanced through-the-wall radar imaging using Bayesian compressive sensing[C]. SPIE, 2013, 8717: 1-12. doi: 10.1117/12.2014814.

WU Q, ZHANG Y D, AMIN M G, et al. Through-the-wall radar imaging based on modified Bayesian compressive sensing[C]. IEEE China Summit Internation Conference on Signal Information Process, Xian, China, 2014: 232-236. doi: 10. 1109/ChinaSIP.2014.6889238.

WU Q, ZHANG Y D, AMIN M G, et al. Multi-static passive SAR imaging based on Bayesian compressive sensing[C]. SPIE Compressive Sensing Conference, Valtimore, MD, USA, 2014: 9109. doi: 10.1117/12.2050524.

庄燕滨, 王尊志, 肖贤建. 基于最大后验概率估计的压缩感知算法[J]. 计算机科学, 2015, 42(11): 279-283.

ZHUANG Yanbin, WANG Zunzhi, and XIAO Xianjian. Reconstruction algorithm in compressed sensing based on maximum posterior estimation[J]. Computer Science, 2015, 42(11): 279-283.

PATI Y C, REZAIIFAR R, KRISHNAPREASAD P S, et al. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition [C]. 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 1993: 40-44.

TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,　2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108.

晋良念, 钱玉彬, 申文亭. 基于改进OMP的超宽带穿墙雷达稀疏成像方法[J]. 计算机技术与应用, 2015, 41(11): 135-139.

JIN Liangnian, QIAN Yubin, and SHEN Wenting. Sparse imaging for ultra-wideband through-the-wall radar based on modified OMP algorithm[J]. Computer Technology and Its Application, 2015, 41(11): 135-139.

DAI W and MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230-2249. doi: 10.1109/TIT.2009.2016006.

HE H, STOICA P, LI J, et al. Designing unimodular sequence sets with good correlations-including an application to MIMO radar[J]. IEEE Transactions on Signal Processing,　2009, 57(11): 4391-4405. doi: 10.1109/TSP.2009.2025108.

甘伟, 许录平, 张华, 等. 一种贪婪自适应压缩感知重构[J]. 西安电子科技大学学报, 2012, 39(3): 50-57.

GAN Wei, XU Luping, ZHANG Hua, et al. Greedy adaptive recovery algorithm for compressed sensing[J]. Journal of Xidian University, 2012, 39(3): 50-57.

刘盼盼, 李雷. 王浩宇. 压缩感知中基于变尺度法的贪婪重构算法的研究[J]. 通信学报, 2014, 35(12): 98-115.

LIU Panpan, LI Lei, and WANG Haoyu. Research on gredddy reconstruction algorithms of compressed sensing based on variable metric method[J]. Journal on Communications, 2014, 35(12): 98-115.

Relative Articles

Supplements(0)

Cited By

Proportional views