An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques

This paper presents a novel robust adaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stoc...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 50; no. 5; pp. 1091 - 1101
Main Authors: Yamada, I., Slavakis, K., Yamada, K.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.2002
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithm
Adaptive filters
Algorithms
Applied sciences
Computational efficiency
Concurrent computing
Convergence of numerical methods
Detection, estimation, filtering, equalization, prediction
Equations
Exact sciences and technology
Filtering algorithms
Half spaces
Information, signal and communications theory
Inspection
Linear equations
Mathematical models
Noise
Noise robustness
Projection
Projection algorithms
Signal and communications theory
Signal, noise
Stochastic resonance
Studies
Telecommunications and information theory
Projection method
Adaptive algorithm
Adaptive filtering
Parallel processing
Signal processing
Iterative method
Set theory
Adaptive estimation
Gradient method
Least mean squares methods
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:This paper presents a novel robust adaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stochastic property closed convex sets by the simple design formulae developed in this paper. A simple set-theoretic inspection also leads to an important statistical reason for the sensitivity to noise of the affine projection algorithm (APA). The proposed adaptive algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces that are highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. The numerical examples show that the proposed adaptive filtering scheme realizes dramatically fast and stable convergence for highly colored excited speech like input signals in severe noise situations.
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ISSN:1053-587X
1941-0476
DOI:10.1109/78.995065