Multistage channel-constrained algorithms for blind source separation using inverse filter criteria

With a given set of multichannel measurements of instantaneous mixture of multiple sources, some blind source separation (BSS) algorithms including the fast kurtosis maximization algorithm (FKMA) and turbo source separation algorithm (TSSA) proposed by Chi et al., can only extract one source signal and the associated column of the mixing matrix A. Separation of all the sources requires a multistage successive cancellation (MSC) procedure resulting in performance degradation due to error propagation effects from stage to stage. In this paper, two novel multistage channel-constrained BSS algorithms, referred to as MCC⊕FKMA and MCC⊕TSSA, are proposed which design the source extraction filter with the constraint of the source extraction filter orthogonal to all the estimated columns of A obtained at all the previous stages, and thus the estimated source signal is free from error propagation effects at each stage. Some simulation results are presented to support that the efficacy of the proposed two novel BSS algorithms.