Filtering and control performance bounds with implications on asymptotic separation

Authors:
Donald L. Snyder;Ian B. Rhodes
Affiliations:
D. Snyder is affiliated with the Department of Electrical Engineering and the Biomedical Computer Laboratory, Washington University, St. Louis, Missouri 63130 USA;I. Rhodes is affiliated with the Laboratory of Control Systems Science and Engineering, Washington University, St. Louis, Missouri 63130 USA
Venue:
Automatica (Journal of IFAC)
Year:
1972

Citing 2
Cited 2

Bounds on the accuracy attainable in the estimation of continuous random processes

IEEE Transactions on Information Theory
Filtering and detection for doubly stochastic Poisson processes

IEEE Transactions on Information Theory

Position USBL/DVL sensor-based navigation filter in the presence of unknown ocean currents

Automatica (Journal of IFAC)
Cone-bounded nonlinearities and mean-square bounds-Smoothing and prediction

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

A bound is derived on the accuracy in causally estimating a Gaussian process from nonlinear observations. Both additive Gaussian noise and Poisson observations are included. The bound is used to study the control of a stochastic linear dynamical system with nonlinear observations of either type and an average quadratic cost. An asymptotic Separation Theorem is established showing that a linear feedback control law, involving a state estimate, is asymptotically optimum as the accuracy of the state estimate approaches the bound.