Contributions to Proceedings:

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, 6269 - 6273.

English 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.

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

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.