학술논문

Generalized bounding filters for linear time invariant systems
Document Type
Conference
Source
1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, 1977 IEEE Conference on. :585-590 Dec, 1977
Subject
Robotics and Control Systems
Computing and Processing
General Topics for Engineers
Nonlinear filters
Time invariant systems
Stochastic processes
Uncertainty
Covariance matrix
Random processes
Vectors
Differential equations
Language
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.

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