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An algorithm for linearly constrained adaptive filtering of nonstationary signals

Victor I. Djigan


A new multichannel RLS-algorithm of adaptive filtering with a sliding window and linear constraints is considered. The algorithm is based on the inverse QR-decomposition of the matrix of input signals of an adaptive filter. A peculiar feature of the algorithm is absence of square root operations in it. The algorithm’s effectiveness is demonstrated by simulation. The simulation concerns a problem of identification of a three-channel linear adaptive filter, whose input signals represent speech signals. The ERLE parameter value obtained by the new algorithm for this problem is roughly by 20 dB larger compared to the linearly constrained RLS-algorithm with an infinite window.

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