基于MS-ROMP信号重构算法的欠定盲分离

doi:10.3969/j.issn.1001-506X.2020.04.04

Abstract

Abstract:

In order to improve the reconstruction accuracy of the least support denosing orthogonal matching pursuit (LSD-OMP) algorithm, short the reconstruction time and improve the performance of the algorithm, a regularized orthogonal matching pursuit based on multiple support (MS-ROMP) is proposed. Since the LSD-OMP algorithm selects only a few atoms to locate the support set and cannot eliminate the wrong atoms added to the support set, the signal recovery accuracy is reduced and the time is increased. To solve this problem, the performance of the algorithm is improved by improving the termination condition and introducing multiple support and regularization. By setting the threshold, eliminating some wrong atoms, and combining some support sets to locate the optimal support set, the source signal is separated from the mixed signal, thus more accurately achieving under determined blind source separation. The effectiveness of the proposed algorithm is verified by simulation experiments.

Key words: compressed sensing, under determined blind source separation, regularization, multiple support, orthogonal matching pursuit (OMP)

CLC Number:

TN911.7

Ce JI, Xiaomeng ZHANG. Regularization orthogonal matching pursuit based on multiple support[J]. Systems Engineering and Electronics, 2020, 42(4): 756-763.

Figures/Tables 11

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References 30

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信号	算法
信号	OMP	LSD-OMP	MS-ROMP
s₁	29.546 1	40.315 6	47.025 0
s₂	31.963 2	45.135 8	45.746 4
s₃	32.165 3	43.273 3	47.696 9

信号	算法
信号	OMP	LSD-OMP	MS-ROMP
s₁	0.010 9	0.002 0	0.001 7
s₂	0.010 7	0.002 1	0.001 4
s₃	0.011 0	0.002 4	0.001 6

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Regularization orthogonal matching pursuit based on multiple support

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