A combined Kalman filter and natural gradient algorithm approach for blind separation of binary distributed sources in time-varying channels

A combined Kalman filter (KF) and natural gradient algorithm (NGA) approach is proposed to address the problem of blind source separation (BSS) in time-varying environments, in particular for binary distributed signals. In situations where the mixing channel is nonstationary, the performance of the NGA is often poor. Typically, in such cases, an adaptive learning rate is used to help the NGA track the changes in the environment. The Kalman filter, on the other hand, is the optimal, minimum mean square error method for tracking certain non-stationarity. Experimental results are presented, and suggest that the combined approach performs significantly better than NGA in the presence of both continuous and abrupt non-stationarities.