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

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基于块稀疏信号的正则化自适应压缩感知算法

庄哲民, 吴力科, 李芬兰, 魏楚亮   

  1. 汕头大学 电子工程系, 广东 汕头 515063
  • 收稿日期:2012-10-28 出版日期:2014-01-01 发布日期:2014-01-01
  • 作者简介:庄哲民（1965-），男，教授，博士.研究方向：智能信号处理，无线传感网络.E-mail：zmzhuang@stu.edu.cn
  • 基金资助:

    国家自然科学基金项目（61070152）；广东省科技计划项目（2007B010400073）；汕头大学科研基金项目（NTF10012）.

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

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摘要：

在研究已有的块稀疏信号贪婪算法的基础上，提出一种正则化的自适应恢复算法。该算法在块稀疏度未知的前提下，添加了正则化的思想进行块挑选，从而更正确地挑选出块信号的支撑块，实现信号的重建。该算法首先在确定块的稀疏度和选择步长后，利用相关最大化原则实现支撑块的初次挑选；然后，依据已挑选出的支撑块再进行正则化分组，实现二次挑选；最终通过循环迭代正确挑选出整个信号的支撑块。通过仿真实验证明，该算法不仅不需要信号的块稀疏度作为先验知识，且较现有的块信号贪婪算法的重构概率更高，也比现有的块稀疏自适应贪婪算法所需的迭代次数更少和迭代时间更短。

关键词: 通信技术, 稀疏信号, 自适应, 正则化, 贪婪算法

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

中图分类号: 

  • TN911.6

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