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Mixed least-mean-squares/H^∞-optimal adaptive filtering

Hassibi, Babak and Kailath, Thomas (1997) Mixed least-mean-squares/H^∞-optimal adaptive filtering. In: Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers, 1996. Vol.1. IEEE , Piscataway, NJ, pp. 425-429. ISBN 0-8186-7646-9. https://resolver.caltech.edu/CaltechAUTHORS:20150218-073848656

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Abstract

We construct a so-called mixed least-mean squares/H-∞optimal (or mixed H^2/H^∞-optimal) algorithm for adaptive filtering. The resulting adaptive algorithm is nonlinear and requires O(n^2) (where n is the number of filter weights) operations per iteration. Such mixed algorithms have the properly of yielding the best average (least-mean-squares) performance over all algorithms that achieve a certain worst-case (H^∞-optimal) bound. They thus allow a tradeoff between average and worst-case performance and are most applicable in situations where the exact statistics and distributions of the underlying signals are not known. Simple simulations are also presented to compare the algorithm's behaviour with standard least-squares and H^∞ adaptive filters.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/ACSSC.1996.600941DOIArticle
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=600941PublisherArticle
Additional Information:© 1996 IEEE. This research was supported by the Advanced Research Projects Agency of the Department of Defense monitored by the Air Force Office of Scientific Research under Contract F49620-93-1-0085.
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)F49620-93-1-0085
Advanced Research Projects Agency (ARPA)UNSPECIFIED
Record Number:CaltechAUTHORS:20150218-073848656
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150218-073848656
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:54915
Collection:CaltechAUTHORS
Deposited By: Shirley Slattery
Deposited On:27 Feb 2015 01:02
Last Modified:03 Oct 2019 08:01

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