Sigma-point multiple particle filtering

Abstract In this paper, we introduce two new particle filtering algorithms for high-dimensional state spaces in the multiple particle filtering approach. In multiple particle filtering, the state space is partitioned and a different particle filter is used for each component of the partition. At each time step, all particle filters share information about their marginal densities so that they can adequately approximate the filtering recursion. In this paper, we propose a second order approximation to the involved densities based on sigma-point integration methods. We then introduce two different particle filters that make use of this strategy. Finally, we demonstrate their remarkable performance through simulations of a multiple target tracking scenario with a sensor network.

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