Improved Quasi-Newton adaptive-filtering algorithm

Authors:
Md Zulfiquar Ali Bhotto;Andreas Antoniou
Affiliations:
Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada;Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
Venue:
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Year:
2010

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Abstract

An improved quasi-Newton (QN) algorithm that performs data-selective adaptation is proposed whereby the weight vector and the inverse of the input-signal autocorrelation matrix are updated only when the a priori error exceeds a prespecified error bound. The proposed algorithm also incorporates. an improved estimator of the inverse of the autocorrelation matrix. With these modifications, the proposed QN algorithm takes significantly fewer updates to converge and yields a reduced steady-state misalignment relative to a known QN algorithm proposed recently. These features of the proposed QN algorithm are demonstrated through extensive simulations. Simulations also show that the proposed QN algorithm, like the known QN algorithm, is quite robust with respect to roundoff errors introduced in fixed-point implementations.