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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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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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

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