Continuous and discrete time filters for Markov jump linear systems with Gaussian observations

TitleContinuous and discrete time filters for Markov jump linear systems with Gaussian observations
Publication TypeConference Paper
Year of Publication1996
AuthorsKrishnamurthy, V., and J. Evans
Conference NameStatistical Signal and Array Processing, 1996. Proceedings., 8th IEEE Signal Processing Workshop on (Cat. No.96TB10004
Pagination402 -405
Date Publishedjun.
Keywordscontinuous time filters, direct numerical solutions, discrete time approximate model, discrete time filters, filtering equations, filtering theory, finite dimensional filters, Gaussian noise, Gaussian observations, linear systems, maneuvering targets, Markov chain, Markov jump linear systems, Markov processes, noisy measurements, passive tracking, robust discretization, simulations, speech coding, state estimation, target tracking, tracking filters

We present new finite dimensional filters for estimating the state of Markov jump linear systems, given noisy measurements of the Markov chain. Discrete time as well as continuous time models are considered. A robust version of the continuous time filters is used to derive a discretization which links the continuous and discrete time results. Simulations compare the robust discretization with direct numerical solutions of the filtering equations. The new filters have applications in the passive tracking of maneuvering targets and speech coding


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