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Improved Quasi-Newton Adaptive-Filtering Algorithm
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DOI：10.1109/TCSI.2009.2038567.png)
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摘要
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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.
Keyword:
Adaptation algorithms
adaptive filters
convergence speed in adaptation algorithms
quasi-Newton algorithms
steady-state misalignment
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