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Nonlinear Distributed Filtering with Different Local Models

We derive the evolution of the conditional density of the state of a Markov process based on all the measurements of the state while using local conditional density functions communicated to the coordinator. The local conditional density functions are the result of local processing based on different valid local models and the local measurements of the state of the process. The derived equation is used to derive the distributed evolution of the first two moments of the central state.