Improved Quasi-Newton Adaptive-Filtering Algorithm
Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC, CanadaCircuits and Systems I: Regular Papers, IEEE Transactions on (Impact Factor: 2.4). 09/2010; 57(8):2109 - 2118. DOI: 10.1109/TCSI.2009.2038567
Source: IEEE Xplore
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.
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ABSTRACT: Two robust quasi-Newton (QN) adaptive filtering algorithms that perform well in impulsive-noise environments are proposed. The new algorithms use an improved estimate of the inverse of the autocorrelation matrix and an improved weight-vector update equation, which lead to improved speed of convergence and steady-state misalignment relative to those achieved in the known QN algorithms. A stability analysis shows that the proposed algorithms are asymptotically stable. The proposed algorithms perform data-selective adaptation, which significantly reduces the amount of computation required. Simulation results presented demonstrate the attractive features of the proposed algorithms.Circuits and Systems II: Express Briefs, IEEE Transactions on 08/2011; 58-II(8):537-541. DOI:10.1109/TCSII.2011.2158722 · 1.23 Impact Factor
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