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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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 57; no. 12; pp. 4660 - 4674
Main Authors: Yukawa, M., de Lamare, R.C., Yamada, I.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.12.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2009.2027397