Robust Reduced-Rank Adaptive Algorithm Based on Parallel Subgradient Projection and Krylov Subspace

In this paper, we propose a novel reduced-rank adaptive filtering algorithm exploiting the Krylov subspace associated with estimates of certain statistics of input and output signals. We point out that, when the estimated statistics are erroneous (e.g., due to sudden changes of environments), the ex...

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Vydané v:	IEEE transactions on signal processing Ročník 57; číslo 12; s. 4660 - 4674
Hlavní autori:	Yukawa, M., de Lamare, R.C., Yamada, I.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.12.2009 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive algorithm Adaptive algorithms Adaptive filters Algorithm design and analysis Algorithms Applied sciences Convergence Detection, estimation, filtering, equalization, prediction Error analysis Exact sciences and technology Filtering algorithms Information, signal and communications theory Krylov subspace Mathematical models Miscellaneous Performance analysis reduced-rank adaptive filtering Robustness set theory Signal and communications theory Signal processing Signal, noise Statistics subgradient methods Subspaces System identification Telecommunications and information theory Tracking Performance evaluation Target tracking Adaptive algorithm Input signal Krylov subspace Adaptive filtering Subspace method Krylov subspace method Output signal Adaptive method Mean square error reduced-rank adaptive filtering Signal processing Numerical simulation Set theory System identification subgradient methods
ISSN:	1053-587X, 1941-0476
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Popis
Shrnutí:	In this paper, we propose a novel reduced-rank adaptive filtering algorithm exploiting the Krylov subspace associated with estimates of certain statistics of input and output signals. We point out that, when the estimated statistics are erroneous (e.g., due to sudden changes of environments), the existing Krylov-subspace-based reduced-rank methods compute the point that minimizes a ldquowrongrdquo mean-square error (MSE) in the subspace. The proposed algorithm exploits the set-theoretic adaptive filtering framework for tracking efficiently the optimal point in the sense of minimizing the ldquotruerdquo MSE in the subspace. Therefore, compared with the existing methods, the proposed algorithm is more suited to adaptive filtering applications. A convergence analysis of the algorithm is performed by extending the adaptive projected subgradient method (APSM). Numerical examples demonstrate that the proposed algorithm enjoys better tracking performance than the existing methods for system identification problems.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2009.2027397