Compressive Sensing MUSIC Algorithm Based on Difference Map

Lü Zhi-feng; Lei Hong

doi:10.11999/JEIT141542

Volume 37 Issue 8

Aug. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(8): 1874-1878

Lü Zhi-feng, 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

Citation:

Lü Zhi-feng, 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

Lü Zhi-feng, 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

Citation:

Lü Zhi-feng, 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

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Compressive Sensing MUSIC Algorithm Based on Difference Map

doi: 10.11999/JEIT141542

Lü Zhi-feng¹,
Lei Hong²

1.
2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Received Date: 2014-12-04
Rev Recd Date: 2015-03-13
Publish Date: 2015-08-19

Abstract

Abstract

The Multiple Measurement Vectors (MMV) problem addresses the recovery of unknown input vectors which share the same sparse support. The Compressed Sensing (CS) has the capability of estimating the sparse support even in coherent cases, where the traditional array processing approaches like MUltiple SIgnal Classification (MUSIC) often fail. However, CS guarantees the accurate recovery in a probabilistic manner, and often shows inferior performance in cases where the traditional ways succeed. Recently, a novel compressive MUSIC (or CS-MUSIC) algorithm is proposed by Kim et al., in which both the advantages of CS and traditional MUSIC-like methods are combined together. As an iterative projecting algorithm, Difference Map (DM) is first used to solve the phase retrieval problem in crystallography. Recent results show that it has excellent performance in solving a wide variety of non-convex problems like compressed sensing. In this paper, a DM-based CS-MUSIC algorithm is proposed. Experiments show that the proposed algorithm is very effective in MMV problem solving and the success rate of CS-MUSIC is dramatically improved.
- Compressed Sensing (CS),
- Multiple Measurement Vectors (MMV) problem,
- Joint sparsity,
- MUltiple SIgnal Classification (MUSIC),
- Difference Map(DM)

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References(34)

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