Underdetermined Blind Source Separation Based on OS-SASP Algorithm

doi:10.12068/j.issn.1005-3026.2021.04.007

Abstract

Abstract: A sparse adaptive subspace pursuit based on the optimal support(OS-SASP)algorithm was proposed to deal with the problem of underdetermined blind source separation based on sparse component analysis. By introducing the idea of self-adaptation， the dependence of the traditional subspace pursuit(SP)algorithm on sparsity was overcome. At the same time， the size of the minimum support set was determined by the energy concentration characteristic of discrete cosine transform before the start of iteration. Further， the optimal support set was obtained by calculating the union of the minimum support sets. And the combination of the optimal support set and the candidate set in the joint iteration was used to locate the best atom， so as to improve the source signal′s restore accuracy. The simulation results showed that the OS-SASP algorithm can achieve promising performance in the underdetermined blind source recovery of the one-dimensional sparse signals and speech signals.

Key words: underdetermined blind source separation; source signal reconstruction; self-adaption; discrete cosine transform; optimize support set; subspace pursuit(SP)

CLC Number:

TN911.7

JI Ce， ZHANG Huan， GENG Rong， LI Bo-qun. Underdetermined Blind Source Separation Based on OS-SASP Algorithm[J]. Journal of Northeastern University(Natural Science), 2021, 42(4): 501-508.

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