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RESEARCH PAPERS

A Decentralized Multiple Model Adaptive Filtering for Discrete-Time Stochastic Systems

[+] Author and Article Information
Keigo Watanabe

College of Engineering, Shizuoka University, Hamamatsu 432, Japan

J. Dyn. Sys., Meas., Control 111(3), 371-377 (Sep 01, 1989) (7 pages) doi:10.1115/1.3153063 History: Received October 10, 1987; Revised October 01, 1988; Online July 21, 2009

Abstract

A decentralized multiple model adaptive filter (MMAF) is proposed for linear discrete-time stochastic systems. The structure of decentralized multiple model studied here is based on introducing a global hypothesis for the global model and a local hypothesis for the local model, where it is assumed that the former hypothesis includes the latter one as a partial element. Algorithms for the decentralized MMAFs in unsteady and steady-state are derived using recent results in decentralized Kalman filtering. The results can be applied in designing a system for sensor failure detection and identification (FDI). An example is included to illustrate the characteristics of such a FDI system for the estimation of lateral dynamics of the hydrofoil boat.

Copyright © 1989 by ASME
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