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

Jafari, MG, Seah, HW and Chambers, JA (2001) A combined Kalman filter and natural gradient algorithm approach for blind separation of binary distributed sources in time-varying channels ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5. pp. 2769-2772.

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Abstract

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 non-stationary, the performance of NGA is often poor. Typically, in such cases, an adaptive learning rate is used to help 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.

Item Type: Article
Authors :
NameEmailORCID
Jafari, MGUNSPECIFIEDUNSPECIFIED
Seah, HWUNSPECIFIEDUNSPECIFIED
Chambers, JAj.a.chambers@surrey.ac.ukUNSPECIFIED
Date : 26 September 2001
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:27
Last Modified : 17 May 2017 13:27
URI: http://epubs.surrey.ac.uk/id/eprint/839270

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