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On The Performance of Gaussian Mixture Estimation Techniques for Dicrete-Time Jump Markov Linear Systems

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3 Author(s)
Elliott, R.J. ; Haskayne Sch. of Bus., Calgary Univ., Alta. ; Dufour, F. ; Malcolm, W.P.

In this article we examine the numerical performance of a new state estimation algorithm for discrete-time Gauss-Markov models, whose parameters are determined at each discrete-time instant by the state of a Markov chain. The scheme we consider is fundamentally distinct from extant methods, such as the so-called interacting multiple model algorithm (IMM) in that it is based directly upon the corresponding exact hybrid filter dynamics. Our new scheme maintains a fixed number of candidate paths in a history, each identified by an optimal subset of estimated mode probabilities. The memory requirements of our filter are fixed in time and can varied by the user to achieve a desired accuracy. Computer simulations are given to demonstrate performance of the Gaussian-mixture algorithm described, against the IMM

Published in:

Decision and Control, 2006 45th IEEE Conference on

Date of Conference:

13-15 Dec. 2006