A Complex-Valued Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation
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This paper considers complex-valued mixing matrix estimation in underdetermined blind source separation. An effective estimation algorithm based on both single-source-point (SSP) detection and modified dynamic data field clustering is proposed. First, array-processing-based time–frequency SSP detection is applied to improve signal sparsity, therein utilizing the real and imaginary components of the observed signals in the time–frequency domain. The algorithm can be applied to the estimation of complex-valued mixing matrix based on L-shaped arrays and uniform circular arrays. Then, to overcome the limitation that the clustering performance of traditional algorithms is affected by noise, data cleansing detection is introduced to reselect the SSPs with high potential energy as representative objects to achieve preliminary data classification. Finally, a dynamic data field clustering algorithm is adopted to move and merge the representative objects until all column vectors of the mixing matrix are estimated. Simulation results show that the proposed method can effectively estimate complex-valued mixing matrices with high accuracy, especially in real-world noncooperative cases without prior knowledge.
KeywordsUnderdetermined blind source separation Complex-valued mixing matrix estimation Data cleansing detection Modified dynamic data field clustering
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 National Key Research and Development Program of China (No. 2016YFC0101700), the Fundamental Research Funds for the Central Universities (No. HEUCF1508), and the Natural Science Foundation of Heilongjiang Province (No. F201337).
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