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On adaptive filtering with combined least-mean-squares and H^∞ criteria

Hassibi, Babak and Kailath, Thomas (1997) On adaptive filtering with combined least-mean-squares and H^∞ criteria. In: Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers, 1997. Vol.2. IEEE , Piscataway, NJ, pp. 1570-1574. ISBN 0-8186-8316-3.

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We study the possibility of combining least-mean-squares, or stochastic, performance with H^∞-optimal, or worst-case, performance in adaptive filtering. The resulting adaptive algorithms allow for a trade-off between average and worst-case performances and are most applicable in situations, such as mobile communications, where, due to modeling errors and rapid time-variation of system parameters, the exact statistics and distributions of the underlying signals are not known. We mention some of the open problems in this field, and construct a nonlinear adaptive filter (requiring O(n^2) operations per iteration, where n is the number of filter weights) that recursively minimizes the least-mean-squares error over all filters that guarantee a specified worst-case H^∞ bound. We also present some simple examples to compare the algorithm's behaviour with standard least-squares and H^∞ adaptive filters.

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Additional Information:© 1997 IEEE. This work was supported in part by DARPA through the Department of Air Force under contract F49620-95-1-0525-P00001 and by the Joint Service Electronics Program at Stanford under contract DAAH04-94-G-0058-P00003.
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Air Force Office of Scientific Research (AFOSR)F49620-95-1-0525-P00001
Joint Service Electronics ProgramDAAH04-94-G-0058-P00003
Record Number:CaltechAUTHORS:20150218-070609299
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:54903
Deposited By: Shirley Slattery
Deposited On:27 Feb 2015 01:07
Last Modified:10 Nov 2021 20:39

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