Sparse Signal Recovery Based on Complex Bayesian Compressive Sensing

WANG Wei; TANG Weimin; WANG Ben; LEI Shujie

doi:10.11999/JEIT151056

Volume 38 Issue 6

Jun. 2016

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WANG Wei, TANG Weimin, WANG Ben, LEI Shujie . Sparse Signal Recovery Based on Complex Bayesian Compressive Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1419-1423. doi: 10.11999/JEIT151056

Citation:

WANG Wei, TANG Weimin, WANG Ben, LEI Shujie . Sparse Signal Recovery Based on Complex Bayesian Compressive Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1419-1423. doi: 10.11999/JEIT151056

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Sparse Signal Recovery Based on Complex Bayesian Compressive Sensing

doi: 10.11999/JEIT151056 cstr: 32379.14.JEIT151056

Funds:

The National Natural Science Foundation of China (61571148), China Postdoctoral Science Foundation (2014M550182), Heilongjiang Province Postdoctoral Special Foundation (LBH-TZ0410), Harbin Science and Technology Innovation Talents (2013RFXXJ016), China Postdoctoral Special Funding (2015T 80328)

Received Date: 2015-09-17
Rev Recd Date: 2016-03-18
Publish Date: 2016-06-19

Abstract

Abstract

An effective Sparse Bayesian Learning algorithm exploiting Complex sparse Temporal correlation (CTSBL) is proposed in this paper, which is used to recover sparse complex signal. By exploiting the fact that the real and imaginary components of a complex value share the same sparsity pattern, it can improve the sparsity of the estimated signal. A multitask sparse signal recovery issue is transformed to a block sparse signal recovery issue of a single measurement by taking full advantage of the internal structure information among the multiple measurement vector signals. The experiments show that the proposed algorithm CTSBL achieves better recovery performance compared with the existing Complex MultiTask Bayesian Compressive Sensing (CMTBCS) algorithm and BOMP algorithm.
- Compressive Sensing (CS),
- Sparse signal recovery,
- Multitask measurement vector model,
- Block sparse Bayes

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References(23)

References

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