NDLI: Generalized bounding filters for linear time invariant systems

Content Provider	IEEE Xplore Digital Library
Author	Greenlee, T.L. Leondes, C.T.
Copyright Year	1977
Description	Author affiliation: Orincon Corporation, La Jolla, Callfornia (Greenlee, T.L.) \|\| University of California, Los Angeles, California (Leondes, C.T.)
Abstract	Weiner and Kalman-Bucy filtering problems assume that the models describing the signal and noise stochastic processes are exactly known a priori. In most practical situations this exact a priori knowledge is not possible and suboptimality results. Nahi and Weiss (1971, 1972) have addressed this problem of uncertainty and suboptimality, for linear time-invariant systems, in their work on bounding filters. A bounding filter is essentially a Wiener filter that is designed using bounding power spectral densities. In this paper, now-stationary disturbances are considered and a technique is developed for designing casual, linear, tlme-invariant filters that have a calculated error covariance which bounds their actual error covariance in an "average" sense. The new filters are termed generalized bounding filters (GBF). A GBF is a "type of" Wiener filter that is designed using bounding "average energy" spectra.
Starting Page	585
Ending Page	590
File Size	407859
Page Count	6
File Format	PDF
DOI	10.1109/CDC.1977.271640
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1977-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear filters Time invariant systems Stochastic processes Uncertainty Covariance matrix Random processes Vectors Differential equations
Content Type	Text
Resource Type	Article

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