Underdetermined Blind Source Separation Based on Locally Sparse Representations

He Chun-lan; Wan Zhe

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Underdetermined Blind Source Separation Based on Locally Sparse Representations

更新时间：2026-05-27

- Underdetermined Blind Source Separation Based on Locally Sparse Representations
- Vol. 25, Issue 4, Pages: 61-64(2008)
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- Published：2008
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何春兰, 万哲. 局部充分稀疏的欠定时盲信号源分离[J]. 广东工业大学学报, 2008, 25(4): 61-64.

He Chun-lan, Wan Zhe. Underdetermined Blind Source Separation Based on Locally Sparse Representations[J]. 2008, 25(4): 61-64.
何春兰, 万哲. 局部充分稀疏的欠定时盲信号源分离[J]. 广东工业大学学报, 2008, 25(4): 61-64. DOI：

He Chun-lan, Wan Zhe. Underdetermined Blind Source Separation Based on Locally Sparse Representations[J]. 2008, 25(4): 61-64. DOI：

摘要

针对欠定时的盲信号分离

提出了局部充分稀疏条件下估计混叠矩阵A的新算法.该算法不要求源信号所有采样时刻都充分稀疏

先通过搜索

把处于同一直线的向量一一归类

再对所得的类的向量进行处理

把混叠矩阵A确定出来.仿真实验结果表明算法是有效的.

Abstract

It proposes a new algorithm of locally sparse blind source separation for estimating the mixed matrix A.This method doesn’t require all the samples of the sources are strictly sparse.Firstly all the vectors in the same line were searched for and classified.And then the mixed matrix A determined.The simulation illustrates the effectiveness of this algorithrn.

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references

肖明,谢胜利,傅予力. 欠定情形下语音信号盲分离的时域检索平均法[J]. 中国科学(E辑:信息科学). 2007(12)

谢胜利,谭北海,傅予力. 基于平面聚类算法的欠定混叠盲信号分离[J]. 自然科学进展. 2007(06)

何昭水,谢胜利,傅予力. 稀疏表示与病态混叠盲分离[J]. 中国科学E辑:信息科学. 2006(08)

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