document: Recursive state estimation for multiple switching models with unknown transition probabilities This work considers hybrid systems with continuous-valued target states and discrete-valued regime variable . The changes -LRB- switches -RRB- of the regime variable are modeled by a finite state Markov chain with unknown and random transition probabilities following Dirichlet distributions . Our work analytically derives the marginal posterior distribution of the states and regime variables , the transition probabilities being integrated out . This leads to a variety of recursive hybrid state estimation schemes which are an appealing intuitive and straightforward extension of standard algorithms . Their performance is illustrated by a maneuvering target tracking example
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