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

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.