Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation

Wang Chuanchuan; Zeng Yonghu; Wang Liandong

doi:10.11884/HPLPB201830.170354

Volume 30 Issue 5

May 2018

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Wang Chuanchuan, Zeng Yonghu, Wang Liandong. Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation[J]. High Power Laser and Particle Beams, 2018, 30: 053202. doi: 10.11884/HPLPB201830.170354

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Wang Chuanchuan, Zeng Yonghu, Wang Liandong. Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation[J]. High Power Laser and Particle Beams, 2018, 30: 053202. doi: 10.11884/HPLPB201830.170354

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Comparison of source signal recovery algorithms based on compressed sensing for underdetermined blind source separation

doi: 10.11884/HPLPB201830.170354

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China

Received Date: 2017-09-05
Rev Recd Date: 2017-11-08
Publish Date: 2018-05-15

Abstract

Abstract

The source signal recovery model for underdetermined blind source separation based on compressed sensing(CS) is constructed, and the recovery effect of three algorithms separately based on the complementary matching pursuit(CMP), the L1 based complementary matching pursuit(L1CMP) and modified Newton radial basis function(NRASR) are compared by simulation. Results show that as to the completely sparse source signals in time domain, the recovery effect of the three algorithms are similar, while the calculation complexity of L1CMP is the lowest. As to the completely sparse source signals in transformation domain, the recovery effects of CMP and L1CMP are similar, but that of NRASR is worse. When the source signals are incompletely sparse in time domain, the recovery effect of CMP is worse, and those of L1CMP and NRASR are similar. So based on comprehensive consideration, the L1CMP algorithm is the best in the three algorithms. As to the case of the source signal number and observation signal number are small, the recovery effect would decline in time domain. The sparse representation method combined with the CS reconstruction algorithms can get good source signal recovery effect.

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References(10)

References

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