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

  • Vesselin P. Jilkov
  • X. Rong Li
  • Donka S. Angelova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2542)

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.

Keywords

Markov Chain Monte Carlo Transition Probability Matrix Finite Mixture Adaptive Estimation Markovian Jump System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Vesselin P. Jilkov
    • 1
  • X. Rong Li
    • 1
  • Donka S. Angelova
    • 2
  1. 1.Department of Electrical EngineeringUniversity of New OrleansNew OrleansUSA
  2. 2.Central Laboratory for Parallel ProcessingBulgarian Academy Of SciencesSofiaBulgaria

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