Fast algorithms with low complexity for adaptive filtering

Authors:
Madjid Arezki;Daoud Berkani
Affiliations:
LATSI Laboratory, Department of Electronic, University of Blida, Algeria;Signal & Communications Laboratory, Department of Electrical Engineering, Algiers, Algeria
Venue:
WSEAS Transactions on Signal Processing
Year:
2009

Citing 5
Cited 0

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Abstract

The numerically stable version of fast recursive least squares (NS-FRLS) algorithms represent a very important load of calculation that needs to be reduced. Its computational complexity is of 8L operations per sample, where L is the finite impulse response filter length. We propose an algorithm for adaptive filtering, while maintaining equilibrium between its reduced computational complexity and its adaptive performances. We present a new (M-SMFTF) algorithm for adaptive filtering with fast convergence and low complexity. It is the result of a simplified FTF type algorithm, where the adaptation gain is obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. This algorithm presents a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal. Its computational complexity is of 6L and this is considerably reduced to 2L+4P when we use a reduced P-size (P≪L) forward predictor.