Distributed Auxiliary Particle Filters Using Selective Gossip

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.