NDLI: New Sequential Partial-Update Least Mean M-Estimate Algorithms for Robust Adaptive System Identification in Impulsive Noise

Content Provider	IEEE Xplore Digital Library
Author	Zhou, Y. Chan, S.C. Ho, K.L.
Copyright Year	1982
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.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	4455
Ending Page	4470
Page Count	16
File Size	682553
File Format	PDF
ISSN	02780046
Volume Number	58
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Signal processing algorithms System identification Least mean squares methods Adaptive filters 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
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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