Abstract
We propose two distributed particle filter (DPF) algorithms for sensor networks with mutually correlated measurement noises at different sensors. With both algorithms, each sensor runs a local particle filter that knows the global (all-sensors) likelihood function and is thus able to compute a global state estimate based on the measurements of all sensors. We propose two alternative distributed, consensus-based methods for computing the global likelihood function at each sensor. Simulation results for a target tracking problem demonstrate that both DPF algorithms exhibit excellent performance, however with very different communications requirements.
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