多測(cè)量向量模型下的修正MUSIC算法

林云; 胡強(qiáng)

doi:10.11999/JEIT180001

多測(cè)量向量模型下的修正MUSIC算法

doi: 10.11999/JEIT180001 cstr: 32379.14.JEIT180001

林云¹,
胡強(qiáng)^2, ,

1.
重慶郵電大學(xué)光電工程學(xué)院 ??重慶 ??400065
2.
重慶郵電大學(xué)通信與信息工程學(xué)院 ??重慶 ??400065

詳細(xì)信息

作者簡(jiǎn)介:
林云：男，1968年生，副教授，研究方向?yàn)閴嚎s感知、自適應(yīng)濾波算法

胡強(qiáng)：男，1993年生，碩士生，研究方向?yàn)閴嚎s感知

通訊作者:
胡強(qiáng)　 huqiang0424@qq.com

中圖分類號(hào): TN911.7
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- 文章訪問(wèn)數(shù): 1614
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- PDF下載量: 75
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-01-02
- 修回日期: 2018-06-04
- 網(wǎng)絡(luò)出版日期: 2018-07-18
- 刊出日期: 2018-11-01

Modified MUSIC Algorithm for Multiple Measurement Vector Models

Yun LIN¹,
Qiang HU^{2
, ,}

1.
College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

摘要

摘要: 壓縮感知多測(cè)量向量(MMV)模型用于解決具有相同稀疏結(jié)構(gòu)的多快拍問(wèn)題，在傳統(tǒng)陣列信號(hào)處理應(yīng)用中多重信號(hào)分類(MUSIC)方法是一種常見的方法，但當(dāng)快拍數(shù)不足(低于稀疏度)時(shí)其性能將急劇惡化。Kim等人(2012)推導(dǎo)出一種修正的MUSIC譜，并將壓縮重構(gòu)方法和MUSIC算法結(jié)合提出壓縮感知MUSIC算法(CS-MUSIC)，能夠有效克服快拍數(shù)不足的問(wèn)題。該文將Kim等人的結(jié)論擴(kuò)展到一般情形，并基于傳統(tǒng)的MUSIC譜和CS-MUSIC譜提出一種修正的MUSIC算法(MMUSIC)。仿真結(jié)果表明所提算法能夠有效克服快拍數(shù)不足的問(wèn)題，并且具有比CS-MUSIC算法和壓縮感知貪婪算法更高的重構(gòu)概率。
- 壓縮感知 /
- 多測(cè)量向量模型 /
- 聯(lián)合稀疏 /
- 多重信號(hào)分類
Abstract: The Compressed Sensing (CS) Multiple Measurement Vector (MMV) model is used to solve multiple snapshots problem with the same sparse structure. MUltiple SIgnal Classification (MUSIC) is a common method in traditional array signal processing applications. However, when the number of snapshots is below sparsity performance will be dramatically deteriorated. Kim et al. derive a modified MUSIC spectral method and propose a Compressed Sensing MUSIC method (CS-MUSIC) combining the compression reconstruction method and the MUSIC algorithm, which can effectively overcome the problem of insufficient snapshot number. In this paper, Kim et al.’s conclusion is extended to the general case, and a Modified MUSIC (MMUSIC) algorithm is proposed based on the traditional MUSIC method and the CS-MUSIC method. The simulation results show that the proposed algorithm can effectively overcome the shortage of snapshots and has a higher reconstruction probability than the CS-MUSIC algorithm and the compressed sensing greedy algorithm.
- Compressed Sensing (CS) /
- Multiple Measurement Vectors (MMV) model /
- Jointly sparsity /
- MUltiple SIgnal Classification (MUSIC)

HTML全文

圖 1 各算法重構(gòu)概率P與稀疏度K的關(guān)系

下載: 全尺寸圖片幻燈片

圖 2 重構(gòu)概率P與觀測(cè)數(shù)M的關(guān)系

下載: 全尺寸圖片幻燈片

圖 3 q值選取對(duì)MMUSIC算法重構(gòu)性能的影響

下載: 全尺寸圖片幻燈片

參考文獻(xiàn)(18)

CANDèS E J and TAO T. Near-optimal signal recovery from random projections: Universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406–5425 doi: 10.1109/TIT.2006.885507

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306 doi: 10.1109/TIT.2006.871582

CANDéS E J, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 53(2): 489–509 doi: 10.1109/TIT.2005.862083

BLANCHARD J D, CERMAK M, HANLE D, et al. Greedy algorithms for joint sparse recovery[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1694–1704 doi: 10.1109/TSP.2014.2301980

CHOI J W, SHIM B, and DING Y. Compressed sensing for wireless communications: Useful tips and tricks[J]. IEEE Communications Surveys and Tutorials, 2017, 19(3): 1527–1550 doi: 10.1109/COMST.2017.2664421

GUO Jie, SONG Bin, and HE Ying. A survey on compressed sensing in vehicular infotainment systems[J]. IEEE Communications Surveys and Tutorials, 2017, 19(4): 2662–2680 doi: 10.1109/COMST.2017.2705027

YANG Lin, SONG Kun, and SIU Yunming. Iterative clipping noise recovery of ofdm signals based on compressed sensing[J]. IEEE Transactions on Broadcasting, 2017, 63(4): 706–713 doi: 10.1109/TBC.2017.2669641

DU Zhaohui, CHEN Xuefeng, ZHANG Han, et al. Compressed-Sensing-based periodic impulsive feature detection for wind turbine systems[J]. IEEE Transactions on Industrial Informatics, 2017, 12(6): 2933–2945 doi: 10.1109/TII.2017.2666840

WU Kai and LIU Jing. Learning large-scale fuzzy cognitive maps based on compressed sensing and application in reconstructing gene regulatory networks[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(6): 1546–1560 doi: 10.1109/TFUZZ.2017.2741444

石要武, 陳淼, 單澤濤, 等. 基于特征空間MUSIC算法的相干信號(hào)波達(dá)方向空間平滑估計(jì)[J]. 吉林大學(xué)學(xué)報(bào)(工學(xué)版), 2017, 47(1): 268–273 doi: 10.13229/j.cnki.jdxbgxb201701039

SHI Yaowu, CHEN Miao, SHAN Zetao, et al. Spatial smoothing technique for coherent signal DOA estimation based on eigen space MUSIC algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(1): 268–273 doi: 10.13229/j.cnki.jdxbgxb201701039

COTTER S F, RAO B D, ENGAN K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors[J]. IEEE Transaction on Signal Processing, 2005, 53(7): 2477–2488 doi: 10.1109/TSP.2005.849172

BRESLER Y. Spectrum-blind sampling and compressive sensing for continuous-index signals[C]. Information Theory and Applications Workshop, San Diego, USA, 2008: 547–554.

SCHMIDT R. 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

KIM J M, LEE O K, and YE J C. Compressive MUSIC: revisiting the link between compressive sensing and array signal processing[J]. IEEE Transactions on Information Theory, 2012, 58(1): 278–301 doi: 10.1109/TIT.2011.2171529

呂志豐, 雷宏. 基于差值映射的壓縮感知MUSIC算法[J]. 電子與信息學(xué)報(bào), 2015, 37(8): 1874–1878 doi: 10.11999/JEIT141542

Lü Zhifeng and LEI Hong. Compressive sensing MUSIC algorithm based on difference map[J]. Journal of Electronics&Information Technology, 2015, 37(8): 1874–1878 doi: 10.11999/JEIT141542

TROPP J A. Algorithms for simultaneous sparse approximation. Part II: convex relaxation[J]. Signal Processing, 2006, 86(3): 589–602 doi: 10.1109/TSP.2016.2637314

WIPF D P and RAO B D. An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transaction on Signal Processing, 2007, 55(7): 3704–3716 doi: 10.1109/TSP.2007.894265

BARANIUK R G, CEVHER V, DUARTE M F, et al. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982–2001 doi: 10.1109/TIT.2010.2040894

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