Advances in cost-reference particle filtering

Recently, we have proposed a particle filtering-type methodology, which we refer to as cost-reference particle filtering (CRPF). Its main feature is that it is not based on any particular probabilistic assumptions regarding the studied dynamic model. The concepts of particles and particle streams, however, are the same in CRPF as in standard particle filtering (SPF), but the probability masses of the particles are replaced with user defined costs. In this paper we propose some modifications of the original CRPF methodology. The changes allow for development of simpler algorithms, which may also be less computationally intensive and possibly more robust. We investigate several variants of CRPF and compare them with SPF. The advantages and disadvantages of the considered algorithms are illustrated and discussed through computer simulations of tracking of multiple targets which move along a two-dimensional space.