On nonlinear filters for mixed H^2/H^∞ estimation
- Creators
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Hassibi, Babak
- Kailath, Thomas
Abstract
We study the problem of mixed least-mean-squares H^∞ -optimal (or mixed H^2/H^∞-optimal) estimation of signals generated by discrete-time, finite-dimensional, linear state-space models. The major result is that, for finite-horizon problems, and when the stochastic disturbances have Gaussian distributions, the optimal solutions have finite-dimensional (i.e., bounded-order) nonlinear state-space structure of order 2n+1 (where n is the dimension of the underlying state-space model). Being nonlinear, the filters do not minimize an H2 norm subject to an H^∞ constraint, but instead minimize the least-mean-squares estimation error (given a certain a priori probability distribution on the disturbances) subject to a given constraint on the maximum energy gain from disturbances to estimation errors. The mixed filters therefore have the property of yielding the best average (least-mean-squares) performance over all filters that achieve a certain worst-case (H^∞) bound
Additional Information
© 1997 IEEE. This work was supported in part by DARPA through the Department of Air Force under contract F49620-95-l-0525-PO0001 and by the Joint Service Electronics Program at Stanford under contract DAAH04-94-G-0058-P00003.Attached Files
Published - 00611970.pdf
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Additional details
- Eprint ID
- 55439
- Resolver ID
- CaltechAUTHORS:20150302-162239004
- Defense Advanced Research Projects Agency (DARPA)
- F49620-95-l-0525-PO0001
- Joint Service Electronics Program
- DAAH04-94-G-0058-P00003
- Created
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2015-03-03Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field