Rao-Blackwellized particle forward filtering backward smoothing with application to blind source separation

Signal Processing（2014）

Cited 3|Views31

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Abstract

It is difficult for standard independent component analysis (ICA) algorithm to extract signals in noise condition. The main contribution of this paper is to apply Rao-Blackwellized particle filter and smoother to noisy ICA model. The proposed method is presented as a two-stage approach. Firstly, noisy signal is modeled by time-varying autoregressive (TVAR) process, and estimated noise-free signal is obtained by particle filtering and smoothing step. After the preprocessing step, Fast ICA algorithm is adopted to separate the denoising data. The enhancement performance of proposed algorithm is evaluated in simulations at last.

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Key words

autoregressive processes,blind source separation,filtering theory,independent component analysis,particle filtering (numerical methods),signal denoising,smoothing methods,rao-blackwellized particle forward filtering backward smoothing,tvar process,fast ica algorithm,independent component analysis algorithm,noise condition,noise-free signal estimation,noisy ica model,signal extraction,smoothing step,time-varying autoregressive process,tvar model,denoising,noisy ica,particle filter

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