基于DBSCAN-R的欠定盲源分离算法

doi:10.12305/j.issn.1001-506X.2026.05.02

Abstract

Abstract:

In response to the issues of insufficient sparsity in the transform domain and low accuracy in mixed matrix estimation in the problem of underdetermined blind source separation, a hybrid matrix estimation algorithm, density based spatial clustering of applications with noise （DBSCAN）-random sample consensus （RanSaC） algorithm （DBSCAN-R） is proposed for uniform linear arrays in time-delay mixed models. The sparse degree of the transform domain is optimized by using the transform matrix, and then the hybrid matrix is estimated by a more accurate DBSCAN-R algorithm. Firstly, the algorithm introduces a transformation matrix to improve sparsity. Secondly, uses the DBSCAN-R algorithm to cluster the data with linear clustering characteristics, and realizes the cluster center of mass correction. Finally, the minimum L₁ norm method is used to reconstruct the source signal. The experimental results show that the DBSCAN-R algorithm proposed reduces the normalized mean-square error of the hybrid matrix estimation by an average of 4.70 dB compared to the traditional DBSCAN algorithms, which has a robust characteristic.

Key words: underdetermined blind source separation, uniform linear array, density spatial clustering, random sample consensus （RanSaC）, robustness

CLC Number:

TN 911.7

Ce JI, Linshan WANG, Hui MOU. Underdetermined blind source separation algorithm based on DBSCAN-R[J]. Systems Engineering and Electronics, 2026, 48(5): 1465-1473.

Figures/Tables 13

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References 27

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源信号	DBSCAN-R	DBSCAN	KM	Hierarchical+KM
$ {\hat s_1}(t) $	0.986 9	0.974 2	0.973 7	0.959 5
$ {\hat s_2}(t) $	0.891 9	0.870 3	0.873 2	0.851 9
$ {\hat s_3}(t) $	0.930 2	0.930 2	0.932 1	0.921 7

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Underdetermined blind source separation algorithm based on DBSCAN-R

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