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Underdetermined Mixing Matrix Estimation Algorithm Based on Single Source Points

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Abstract

This paper considers the mixing matrix estimation in the underdetermined blind source separation. An effective estimation algorithm based on local directional density detection (LDDD) and dynamic data field clustering (DDFC) is proposed. First, argument-based time-frequency single source points detection is employed to improve signal sparsity. To overcome the limitation of traditional clustering algorithms, which depend on the preset of initial clustering centers and the number of sources, the LDDD is introduced to choose the single source points with high potential energy as representative objects to form data preliminary classification. Then DDFC algorithm is adopted to move and merge the representative objects until all column vectors of mixing matrix are estimated. Simulation results show that the proposed method can effectively estimate mixing matrix with high accuracy, especially in the real non-cooperative cases where the number of sources is unknown.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61371172), the International S&T Cooperation Program of China (ISTCP) (No. 2015DFR10220), the Ocean Engineering Project of the National Key Laboratory Foundation (No. 1213), the Fundamental Research Funds for the Central Universities (No. HEUCF1508), the Natural Science Foundation of Heilongjiang Province (No. F201337).

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Guo, Q., Ruan, G. & Nan, P. Underdetermined Mixing Matrix Estimation Algorithm Based on Single Source Points. Circuits Syst Signal Process 36, 4453–4467 (2017). https://doi.org/10.1007/s00034-017-0522-9

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  • DOI: https://doi.org/10.1007/s00034-017-0522-9

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