ADVANCES IN COST-REFERENCE PARTICLE FILTERING

Recently, we have proposed a particle filtering-type method-ology, which we refer to as cost-reference particle filtering (CRPF). Its main feature is that it is not based on any partic-ular probabilistic assumptions regarding the studied dynamic model. The concepts of particles and particle streams, how-ever, 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 modi-fications of the original CRPF methodology. The changes al-low 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 consid-ered algorithms are illustrated and discussed through com-puter simulations of tracking of multiple targets which move along a two-dimensional space. 1