Title :
New Sequential Partial-Update Least Mean M-Estimate Algorithms for Robust Adaptive System Identification in Impulsive Noise
Author :
Zhou, Y. ; Chan, S.C. ; Ho, K.L.
Author_Institution :
Key Lab. of Noise & Vibration Res., Chinese Acad. of Sci., Beijing, China
Abstract :
The sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms.
Keywords :
Gaussian noise; active noise control; adaptive control; automobiles; control nonlinearities; echo; filtering theory; identification; impulse noise; least mean squares methods; stability; transient response; Gaussian input; acoustic application; acoustic echo cancellation; active noise cancellation; additive Gaussian noise; arithmetic complexity reduction; automobile; contaminated Gaussian noise; convergence performance analysis; double talk; impulse response; impulsive noise environment; industrial informatics application; input normalization; nonlinearity; robust adaptive system identification; robust filtering; robustness; sequential partial-update least mean m-estimate algorithm; Adaptive filters; Algorithm design and analysis; Least mean squares methods; Signal processing algorithms; System identification; Adaptive echo cancellation (AEC); adaptive noise cancellation (ANC); double-talk; impulsive noise; least mean M-estimate (LMM); least mean square (LMS); partial-update adaptive filters; performance analysis; system identification;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2010.2098359