Filtering for Linear Stochastic Systems With Small Measurement Noise

[+] Author and Article Information
Z. Aganovic, Z. Gajic

Rutgers University, Department of Electrical and Computer Engineering, Piscataway, NJ 08855-0909

X. Shen

Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada N2L 3G1

J. Dyn. Sys., Meas., Control 117(3), 425-429 (Sep 01, 1995) (5 pages) doi:10.1115/1.2799135 History: Received April 09, 1994; Revised June 27, 1994; Online December 03, 2007


In this paper we present a method which produces complete decomposition of the optimal global Kalman filter for linear stochastic systems with small measurement noise into exact pure-slow and pure-fast reduced-order optimal filters both driven by the system measurements. The method is based on the exact decomposition of the global small measurement noise algebraic Riccati equation into exact pure-slow and pure-fast algebraic Riccati equations. An example is included in order to demonstrate the proposed method.

Copyright © 1995 by The American Society of Mechanical Engineers
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