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Random finite sets are natural representations of multi-target states and observations that allow multi-sensor multi-target tracking to fit in the unifying random set framework for Data Fusion. Although a rigorous foundation has been developed in the form of Finite Set Statistics, optimal Bayesian multi-target filtering is not yet practical. Sequential Monte Carlo (SMC) approximations of the optimal filter are computationally expensive. A practical alternative to the optimal filter is the Probability Hypothesis Density (PHD) filter, which propagates the PHD or first moment instead of the full multi-target posterior. The propagation of the PHD involves multiple integrals which do not admit closed form. We propose to approximate the PHD by a set of weighted random samples which are propagated over time using a generalised SMC method. The resulting algorithm is very attractive as it is general enough to handle non-linear non-Gaussian dynamics and the computational complexity is independent of the (time-varying) number of targets.
Vo et al. (Wed,) studied this question.