Distributed Monte Carlo information fusion and distributed particle filtering
Date
2014
Authors
Manuel, Isaac
Bishop, Adrian
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International Federation of Automatic Control (IFAC)
Abstract
We present a Monte Carlo solution to the distributed data fusion problem and apply it to distributed particle filtering. The consensus-based fusion algorithm is iterative and it involves the exchange and fusion of empirical posterior densities between neighbouring agents. As the fusion method is Monte Carlo based it is naturally applicable to distributed particle filtering. Furthermore, the fusion method is applicable to a large class of networks including networks with cycles and dynamic topologies. We demonstrate both distributed fusion and distributed particle filtering by simulating the algorithms on randomly generated graphs.
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Proceedings of the 19th IFAC World Congress, 2014
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Conference paper
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2037-12-31
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