A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures.
Lecture Notes in Computer Science(2017)
摘要
In the context of Post-Nonlinear (PNL) mixtures, source separation can be performed in a two-stage approach, which encompasses a nonlinear and a linear compensation part. In the former part, it is usually required the knowledge of all the source distributions. In this work, we propose a less restrictive approach, where only one source distribution is needed to be known-here, chosen to be a colored Gaussian. The other sources are only required to present a time structure. The method combines, in a joint-based approach, the use of the second-order statistics (SOS) and the matching of distributions, which shows to be less costly than the classical method of computing the marginal entropy for all sources. The simulation results are favorable to the proposal.
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关键词
Blind source separation,Post-Nonlinear mixtures,Second-order statistics,Density matching
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