吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (01): 259-263.doi: 10.13229/j.cnki.jdxbgxb201401042

• paper • Previous Articles     Next Articles

Regularized adaptive matching pursuit algorithm of compressive sensing based on block sparsity signal

ZHUANG Zhe-min, WU Li-ke, LI Fen-lan, WEI Chu-liang   

  1. Department of Electronics, Shantou University, Shantou 515063, China
  • Received:2012-10-28 Online:2014-01-01 Published:2014-01-01

RICH HTML

0   

PDF (PC)

391

Abstract:

A regularized adaptive matching pursuit algorithm as proposed after research and summarize the existing greedy algorisms based on block-sparse signal. This algorithm mainly in the light of regularized method under a condition that a block-sparse degree is unknown, so that the signal support set can be determined more accurately by the algorithm, then we can reconstruct a signal precisely. First, the algorithm initializes a sparsity degree and step size of a block signal; by maximizing the correlation between residual and measurement matrix, it realizes the selection of subset of the signal support. Then the algorithm updates the selected subset in the second time. Finally, the exact support set is acquired through iteration. The experimental results prove that the proposed algorithm can get better reconstruction performance than other existing greedy algorithms based on block signal, and it has less iteration number and iteration time than the other adaptive algorithm based on block signal.

Key words: communication, block signal, adaptive, regularized, greedy algorithm

CLC Number: 

  • TN911.6

[1] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.

[2] Gao Qiang, Duan Chen-dong, Fang Xiang-bo, et al. A study on matching pursuit based on genetic algorithm[C]//Third International Conference on Measuring Technology and Mechatronics Automation, 2011:283-286.

[3] Tony Cai T, Wang Lei. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory, 2011, 57(7):4680-4688.

[4] Needell D, Vershynin R. Greedy signal recovery and uncertainty principles[C]//Proceedings of the Conference on Computational Imaging, San Jose, USA, SPIE, 2008: 1-12.

[5] Do T T, Lu G, Nam N, et al. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]//The 42nd Asilomar Conference on Signals, Systems and Computers, 2008.

[6] Eldar Y C, Kuppinger P, Bolcskei H. Compressed sensing of block-sparse signal:uncertainly relations and efficient recovery[J]. IEEE Trans on Signal Processing, 2010, 58(6):3042-3054.

[7] Baraniuk R, Ceveher V, Duarte M, et al. Model-based compressive sensing[J].IEEE Trans on Information Theory, 2010, 56(4):1982-2001.

[8] 付宁, 乔立岩, 曹离然. 面向压缩感知的块稀疏度自适应迭代算法[J]. 电子学报, 2011, 39(3):75-79. Fu Ning, Qiao Li-yan, Cao Li-ran. Block sparsity adaptive iteration algorithm for compressed sensing[J]. Acta Electronica Sinica, 2011, 39(3):75-79.

[9] Eldar Y C. Block-sparse signal:uncertainty relations and efficient recovery[J]. IEEE Trans on Signal Processing, 2010, 58(6):3042-3054.

[10] Eldar Y C, Mishali M. Block sparsity and sampling over a union of subspaces[C]//The 16th International Conference on Digital Signal Processing, Santorini, Greece, 2009.

[11] Zhang Z, Rao B D. Recovery of block signals using the framework of block sparse Bayesian learning[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2012:3325-3348.

[12] Xu Tao, Wang Wen-wu. A block-baesd compressed sensing method for underdetermined blind speech separation incorporating binary mask[C]//IEEE International Conference on Acoustics speech and Signal Processing, 2010:2022-2025.
[1] CHEN Yong-heng,LIU Fang-hong,CAO Ning-bo. Analysis of conflict factors between pedestrians and channelized right turn vehicles at signalized intersections [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1669-1676.
[2] CHANG Shan,SONG Rui,HE Shi-wei,LI Hao-dong,YIN Wei-chuan. Recycling model of faulty bike sharing [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1677-1684.
[3] QU Da-yi,YANG Jing-ru,BING Qi-chun,WANG Wu-lin,ZHOU Jing-chun. Arterial traffic offset optimization based on queue characteristics at adjacent intersections [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1685-1693.
[4] GU Wan-li,WANG Ping,HU Yun-feng,CAI Shuo,CHEN Hong. Nonlinear controller design of wheeled mobile robot with H∞ performance [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1811-1819.
[5] ZHOU Yan-guo,ZHANG Hai-lin,CHEN Rui-rui,ZHOU Tao. Two-level game approach based resource allocation scheme in cooperative networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1879-1886.
[6] ZHAO Wei-qiang, GAO Ke, WANG Wen-bin. Prevention of instability control of commercial vehicle based on electric-hydraulic coupling steering system [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1305-1312.
[7] LIU Xiang-yu, YANG Qing-fang, KUI Hai-lin. Traffic guidance cell division based on random walk algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1380-1386.
[8] LIU Zhao-hui, WANG Chao, LYU Wen-hong, GUAN Xin. Identification of data characteristics of vehicle running status parameters by nonlinear dynamic analysis [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1405-1410.
[9] LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[10] LUAN Xin, DENG Wei, CHENG Lin, CHEN Xin-yuan. Mixed Logit model for understanding travel mode choice behavior of megalopolitan residents [J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[11] ZHAO Hong-wei, LIU Yu-qi, DONG Li-yan, WANG Yu, LIU Pei. Dynamic route optimization algorithm based on hybrid in ITS [J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] SUN Xiao-ying, HU Ze-zheng, YANG Jin-peng. Assessment method of electromagnetic pulse sensitivity of vehicle engine system based on hierarchical Bayesian networks [J]. 吉林大学学报(工学版), 2018, 48(4): 1254-1264.
[13] DONG Ying, CUI Meng-yao, WU Hao, WANG Yu-hou. Clustering wireless rechargeable sensor networks charging schedule based on energy prediction [J]. 吉林大学学报(工学版), 2018, 48(4): 1265-1273.
[14] MOU Zong-lei, SONG Ping, ZHAI Ya-yu, CHEN Xiao-xiao. High accuracy measurement method for synchronous triggering pulse transmission delay in distributed test system [J]. 吉林大学学报(工学版), 2018, 48(4): 1274-1281.
[15] DING Ning, CHANG Yu-chun, ZHAO Jian-bo, WANG Chao, YANG Xiao-tian. High-speed CMOS image sensor data acquisition system based on USB 3.0 [J]. 吉林大学学报(工学版), 2018, 48(4): 1298-1304.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd