Title :
A new best fitting Gaussian performance measure for jump Markov systems
Author :
Morelande, Mark ; Ristic, Branko ; Hernandez, Marcel
Author_Institution :
Dept. of EEE, Univ. of Melbourne, Melbourne, VIC
fDate :
June 30 2008-July 3 2008
Abstract :
We consider the problem of estimator performance prediction in stochastic systems with Markov switching dynamical models. Following Hernandez et al, a new best-fitting Gaussian performance measure (BFG-PM) for jump Markov systems is proposed. The new BFG-PM matches the moments of the state transition density of the Markov switching system and the approximate uni-modal system. The new BFG-PM has a state-dependent process noise covariance matrix, hence its recursive computation is carried out via a new formulation of the Cramer-Rao bound for nonlinear filtering with state dependent noise statistics. The paper presents two numerical examples where the existing BFG-PM and the new BFG-PM are compared against the error performance of a typical state estimator for jump Markov systems.
Keywords :
Gaussian processes; Markov processes; covariance matrices; nonlinear filters; stochastic systems; Cramer-Rao bound; Markov switching dynamical model; approximate unimodal system; best-fitting Gaussian performance measure; jump Markov systems; nonlinear filtering; state dependent noise statistics; state transition density; state-dependent process noise covariance matrix; stochastic systems; Cramér-Rao bound; Nonlinear filtering; jump Markov system; manoeuvring target; state dependent covariance;
Conference_Titel :
Information Fusion, 2008 11th International Conference on
Conference_Location :
Cologne
Print_ISBN :
978-3-8007-3092-6
Electronic_ISBN :
978-3-00-024883-2
