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O. Hlinka, F. Hlawatsch:
"Distributed particle filtering in the presence of mutually correlated sensor noises";
in: "Proc. IEEE ICASSP 2013", IEEE - Institute of Electrical and Electronics Engineers, Inc., 2013, S. 6269 - 6273.

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.

distributed particle filter, correlated sensor noises, consensus, distributed target tracking, sensor network

http://publik.tuwien.ac.at/files/PubDat_218912.pdf