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A novel mixing matrix estimation method for underdetermined blind source separation based on sparse subspace clustering

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Abstract

It is essential to accurately estimate the mixing matrix and determine the number of source signals in the problem of underdetermined blind source separation. The problem is solved in this paper via sparse subspace clustering, which can be used to found low-dimensional data structures in observed data. To enhance the linear clustering characteristics of time-frequency points, the high energy points are reserved first, and the angle difference of real and imaginary portions is employed to screen single source points. After that, the time-frequency points are clustered using sparse subspace clustering, and the number of source signals is identified. Finally, the local density of eigenvectors is used to determine the mixing matrix. The proposed algorithm is capable of accurately estimating the mixing matrix. It has strong robustness and adaptable to a wide range of mixing circumstances. The proposed method’s effectiveness is demonstrated by theoretical analysis and experimental data.

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Author notes

  1. Yiqiong Zhang and Yuduo Wang have contributed equally to this work.

Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Qingyi Wang, Yiqiong Zhang & Yuduo Wang

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Qingyi Wang, Yiqiong Zhang & Yuduo Wang

  3. Wuhan Second Ship Design and Research Institute, Wuhan, 430064, China

    Genping Wu

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  1. Qingyi Wang
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  2. Yiqiong Zhang
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  3. Yuduo Wang
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  4. Genping Wu
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Correspondence to Qingyi Wang.

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Wang, Q., Zhang, Y., Wang, Y. et al. A novel mixing matrix estimation method for underdetermined blind source separation based on sparse subspace clustering. SIViP 17, 91–98 (2023). https://doi.org/10.1007/s11760-022-02207-1

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  • DOI: https://doi.org/10.1007/s11760-022-02207-1

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