Distributed auxiliary particle filters using selective gossip

TitleDistributed auxiliary particle filters using selective gossip
Publication TypeConference Proceedings
Year of Publication2011
AuthorsÜstebay, D., M. J. Coates, and M. G. Rabbat
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Pagination3296 - 3299
Date PublishedMay 2011
Conference LocationPrague, Czech Republic
Keywordsdistributed computation, gossip algorithms, Particle filters, target tracking

This paper introduces a distributed auxiliary particle filter for target tracking in sensor networks. Nodes maintain a shared particle filter by coming to a consensus about the likelihoods associated with each particle using the selective gossip procedure. Selective gossip provides a mechanism to efficiently identify the particles with largest weights and focus communication on sharing these important weights. We demonstrate through simulations that the algorithm performs well; compared to state-of-the-art approaches it either significantly improves the accuracy at the expense of a small increase in communication overhead, or achieves comparable accuracy with much lower communication overhead.