General solution and approximate implementation of the multisensor multitarget CPHD filter

Random finite set (RFS) based filters such as the cardinalized probability hypothesis density (CPHD) filter have been successfully applied to the problem of single sensor multitarget tracking. Various multisensor extensions of these filters have been proposed in the literature, but exact update equations for the multisensor CPHD filter have not been identified. In this paper, we provide the update equations and propose an approximate implementation. The exact implementation of the multisensor CPHD filter is infeasible even for very simple scenarios. We develop an algorithm that greedily searches for the most likely groups of measurement subsets. This enables a computationally tractable implementation. Numerical simulations are performed to compare the proposed filter implementation with other random finite set based filters.

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