Estimation of Markovian Jump Systems with Unknown Transition Probabilities through Bayesian Sampling

Authors:
Vesselin P. Jilkov;X. Rong Li;Donka S. Angelova
Affiliations:
-;-;-
Venue:
NMA '02 Revised Papers from the 5th International Conference on Numerical Methods and Applications
Year:
2002

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Abstract

Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors' results, proposed is a new TPMestimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures' estimation. Monte Carlo simulation results of TMP-adaptive interacting multiple model algorithms for a system with failures and maneuvering target tracking are presented.