Underdetermined Blind Source Separation Based on OS-SASP Algorithm

被引：0
|
作者：
Ji C. [1 ]
Zhang H. [1 ]
Geng R. [1 ]
Li B.-Q. [1 ]
机构：
[1] School of Computer Science & Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2021年 / 42卷 / 04期
关键词：
Discrete cosine transform; Optimize support set; Self-adaption; Source signal reconstruction; Subspace pursuit(SP); Underdetermined blind source separation;
D O I：
10.12068/j.issn.1005-3026.2021.04.007
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：501 / 508
页数：7
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