基于混合匹配追蹤算法的MIMO雷達(dá)稀疏成像方法

王偉; 張斌; 李欣

doi:10.11999/JEIT151453

基于混合匹配追蹤算法的MIMO雷達(dá)稀疏成像方法

doi: 10.11999/JEIT151453

王偉¹,
張斌¹,
李欣¹

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61571148)，中國(guó)博士后特別資助(2015T80328)，中國(guó)博士后科學(xué)基金(2014M550182)，黑龍江省博士后特別資助(LBH-TZ0410)，哈爾濱市科技創(chuàng)新人才專項(xiàng)(2013RFXXJ016)

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- 文章訪問(wèn)數(shù): 1881
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出版歷程
- 收稿日期: 2015-12-22
- 修回日期: 2016-06-17
- 刊出日期: 2016-10-19

An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit

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)

摘要

摘要: 多輸入多輸出(MIMO)雷達(dá)作為一種新型的雷達(dá)體制，其成像兼具高分辨率與實(shí)時(shí)性的優(yōu)點(diǎn)。由于觀測(cè)區(qū)域的稀疏性，MIMO雷達(dá)成像可以用壓縮感知的方法進(jìn)行處理。而現(xiàn)有的MIMO雷達(dá)稀疏成像的貪婪恢復(fù)算法中，正交匹配追蹤算法(OMP)存在成像圖像有偽影的缺點(diǎn)，子空間追蹤算法(SP)則受到低分辨率的困擾。針對(duì)上述問(wèn)題，該文提出一種稱為混合匹配追蹤算法的壓縮感知貪婪算法以實(shí)現(xiàn)MIMO雷達(dá)稀疏成像。通過(guò)將兩種貪婪恢復(fù)算法結(jié)合起來(lái)，利用OMP 算法選擇基信號(hào)的正交性和SP 算法具有基信號(hào)選擇的回溯策略，來(lái)重構(gòu)出高分辨率且沒(méi)有偽影的雷達(dá)圖像。仿真實(shí)驗(yàn)驗(yàn)證了所提算法的有效性。
- MIMO雷達(dá) /
- 壓縮感知 /
- 稀疏成像 /
- 貪婪算法
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

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參考文獻(xiàn)(34)

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.

劉濤. MIMO雷達(dá)技術(shù)及其應(yīng)用研究[J]. 無(wú)線互聯(lián)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.

王偉, 馬躍華, 王咸鵬. 一種高運(yùn)算效率的MIMO雷達(dá)BP成像算法[J]. 系統(tǒng)工程與電子技術(shù), 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.

楊杰, 廖桂生, 李軍. 基于波形選擇的MIMO雷達(dá)三維稀疏成像與角度誤差校正方法[J]. 電子與信息學(xué)報(bào). 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.

丁麗. MIMO雷達(dá)稀疏成像的失配問(wèn)題研究[D]. [博士論文], 中國(guó)科學(xué)技術(shù)大學(xué), 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.

王偉, 馬躍華, 郝燕玲. 基于MAPC-RISR的MIMO雷達(dá)距離-角度二位超分辨率成像算法[J]. 中國(guó)科學(xué): 信息科學(xué), 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.

莊燕濱, 王尊志, 肖賢建. 基于最大后驗(yàn)概率估計(jì)的壓縮感知算法[J]. 計(jì)算機(jī)科學(xué), 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.

晉良念, 錢(qián)玉彬, 申文亭. 基于改進(jìn)OMP的超寬帶穿墻雷達(dá)稀疏成像方法[J]. 計(jì)算機(jī)技術(shù)與應(yīng)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.

甘偉, 許錄平, 張華, 等. 一種貪婪自適應(yīng)壓縮感知重構(gòu)[J]. 西安電子科技大學(xué)學(xué)報(bào), 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.

劉盼盼, 李雷. 王浩宇. 壓縮感知中基于變尺度法的貪婪重構(gòu)算法的研究[J]. 通信學(xué)報(bào), 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.

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