Association-free direct filtering of multi-target random finite sets with set distance measures

We consider association-free tracking of multiple targets without identities. The uncertain multi-target state and the uncertain measurements cannot be described by a random vector as this would imply a certain order. Instead, they are described by an unordered random finite set (RFS). Particle-based random finite set densities are used for characterizing the RFS in a simple and natural way. For recursive Bayesian filtering, optimal multi-target state estimates are calculated by systematically minimizing an appropriate set distance measure while directly operating on the particles. Although methods for calculating point estimates of random finite set densities based on appropriate distance measures are available in literature, the proposed recursive filtering is a novel contribution.