Journal
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
Volume 36, Issue 11, Pages 4453-4467Publisher
SPRINGER BIRKHAUSER
DOI: 10.1007/s00034-017-0522-9
Keywords
Underdetermined blind source separation; Mixing matrix estimation; Local directional density detection; Dynamic data field clustering
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Funding
- National Natural Science Foundation of China [61371172]
- International S&T Cooperation Program of China (ISTCP) [2015DFR10220]
- Ocean Engineering Project of the National Key Laboratory Foundation [1213]
- Fundamental Research Funds for the Central Universities [HEUCF1508]
- Natural Science Foundation of Heilongjiang Province [F201337]
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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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