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雷達(dá)高分辨率緊湊感知矩陣追蹤算法

劉靜,  盛明星,  宋大偉,  尚社,  韓崇昭

劉靜, 盛明星, 宋大偉, 尚社, 韓崇昭. 雷達(dá)高分辨率緊湊感知矩陣追蹤算法[J]. 電子與信息學(xué)報, 2016, 38(8): 1950-1955. doi: 10.11999/JEIT151135
引用本文: 劉靜, 盛明星, 宋大偉, 尚社, 韓崇昭. 雷達(dá)高分辨率緊湊感知矩陣追蹤算法[J]. 電子與信息學(xué)報, 2016, 38(8): 1950-1955. doi: 10.11999/JEIT151135
LIU Jing, SHENG Mingxing, SONG Dawei, SHANG She, HAN Chongzhao. Compact Sensing Matrix Pursuit Algorithm for Radars with High Resolution[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1950-1955. doi: 10.11999/JEIT151135
Citation: LIU Jing, SHENG Mingxing, SONG Dawei, SHANG She, HAN Chongzhao. Compact Sensing Matrix Pursuit Algorithm for Radars with High Resolution[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1950-1955. doi: 10.11999/JEIT151135

雷達(dá)高分辨率緊湊感知矩陣追蹤算法

doi: 10.11999/JEIT151135 cstr: 32379.14.JEIT151135
  • 1.

    (西安交通大學(xué)電信學(xué)院 西安 710049) ②(空間微波技術(shù)國家級重點(diǎn)實(shí)驗(yàn)室 西安 710000)

基金項目: 

CAST創(chuàng)新基金(J20141110)，國家自然科學(xué)基金(61573276)，國家973計劃(2013CB329405)

Compact Sensing Matrix Pursuit Algorithm for Radars with High Resolution

  • 1.

    (School of Electronics and Information Engineering, Xi&rsquo

  • 2.

    (National Key Laboratory of Science and Technology on Space Microwave, Xi&rsquo

Funds: 

The Innovation Foundation of CAST (J20141110), The National Natural Science Foundation of China (61573276), The National 973 Program of China (2013CB329405)

  • 摘要: 針對壓縮感知雷達(dá)的感知矩陣相干系數(shù)隨分辨率增加而增大以致不能以大概率對稀疏向量進(jìn)行完美重構(gòu)的問題，直接基于原始感知矩陣，提出緊湊感知矩陣追蹤(CSMP)算法。該文將CSMP算法應(yīng)用于十字陣?yán)走_(dá)的2維波達(dá)方向(DOA)估計并進(jìn)行了計算機(jī)仿真。仿真結(jié)果表明與多信號分類(MUSIC)算法，子空間追蹤(SP)算法，基追蹤(BP)算法和稀疏貝葉斯學(xué)習(xí)(SBL)算法相比，基于CSMP算法的DOA估計分辨率得到了較大提高。
    Abstract: In this paper, a novel algorithm named Compact Sensing Matrix Pursuit (CSMP) is proposed to deal with the high coherence problem encountered in the compressed sensing based radar system with high resolution. The CSMP algorithm is applied to the two dimensional Direction Of Arrival (DOA) estimation of cross-array. The simulation results show that the resolution can be increased largely compared with the MUltiple SIgnal Classification (MUSIC) algorithm, Subspace Pursuit (SP), Basis Pursuit (BP), and the Sparse Bayesian Learning (SBL) algorithms.
    • HTML全文
  • DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109 /TIT.2006.871582.
    ZEINALKHANI C and BANIHASHEMI A H. Iterative reweighted l2/l1 recovery algorithms for compressed sensing of block sparse signals[J]. IEEE Transactions on Signal Processing, 2015, 63(17): 4516-4531. doi: 10.1109/TSP.2015. 2441032.
    BARANIUK R and STEEGHS P. Compressive radar imaging[C]. IEEE Radar Conference, Boston, 2007: 128-133. doi: 10.1109/RADAR.2007.374203.
    BAR-ILAN O and ELDAR Y C. Sub-Nyquist radar via doppler focusing[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1796-1811. doi: 10.1109/TSP.2014.2304917.
    LI Hongtao, WANG Chaoyu, WANG Ke, et al. High resolution range profile of compressive sensing radar with low computational complexity[J]. IET Radar, Sonar and Navigation, 2015, 9(8): 984-990. doi: 10.1049/iet-rsn.2014. 0454.
    BOURGAIN J, DILWORTH S, FORD K, et al. Explicit constructions of RIP matrices and related problems[J]. Duke Mathematical Journal, 2011, 159(1): 145-185. doi: 10.1215/ 00127094-1384809.
    CHEN C and VAIDYANATHAN P. Compressed sensing in MIMO radar[C]. Asilomar Conference on Signal, Systems and Computers, Piscataway, 2008: 41-44.
    SONG Xiaofeng, ZHOU Shengli, and WILLETT P. The role of the ambiguity function in compressed sensing radar[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Dallas, 2010: 2758-2761. doi: 10.1109/ ICASSP.2010.5496221.
    王超宇, 梅湄, 朱曉華, 等. 一種穩(wěn)健的盲稀疏度壓縮感知雷達(dá)目標(biāo)參數(shù)估計方法[J]. 電子與信息學(xué)報, 2014, 36(4): 960-966. doi: 10.3724/SP.J.1146.2013.01007.
    WANG Chaoyu, MEI Mei, ZHU Xiaohua, et al. A robust
    blind sparsity target parameter estimation algorithm for compressive sensing radar[J]. Journal of Electronics Information Technology, 2014, 36(4): 960-966. doi: 10.3724/ SP.J.1146.2013.01007.
    KIM Y G and LEE M J. Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization[J]. IEEE Communications Magazine, 2014, 52(1): 122-129.
    SCHMIDT R O. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276-280. doi: 10.1109/TAP. 1986.1143830.
    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
    ZHU Hao, GEERT L, and GEORGIOS G. Sparsity-cognizant total least-squares for perturbed compressive sampling[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2002-2016. doi: 10.1109/TSP.2011.2109956.
    JI Shihao, XUE Ya, and CARIN L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2346-2356. doi: 10.1109/TSP.2007.914345.
    EVERITT B, LANDAU S, and LEESE M. Cluster Analysis[M]. London: Edward Arnold, 2001: 121-134.
    DONOHO D L, ELAD M, and TEMLYAKOV V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on Information Theory, 2006, 51(1): 6-18. doi: 10.1109/TIT.2005.860430.
    林波, 張增輝, 朱炬波. 基于壓縮感知的DOA估計稀疏化模型與性能分析[J]. 電子與信息學(xué)報, 2014, 36(3): 589-594. doi: 10.3724/SP.J.1146.2013.00149.
    LIN Bo, ZHANG Zenghui, and ZHU Jubo. Sparsity model and performance analysis of DOA estimation with compressive sensing[J]. Journal of Electronics Information Technology, 2014, 36(3): 589-594. doi: 10.3724/SP.J.1146. 2013.00149.
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出版歷程
  • 收稿日期:  2015-10-10
  • 修回日期:  2016-04-22
  • 刊出日期:  2016-08-19

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