Maneuvering target tracking using cost reference particle filtering

Target tracking is a highly nonlinear problem that has been successfully addressed in recent years using sequential Monte Carlo (SMC) methods, usually called particle filters. In this paper, we investigate the application of a new class of SMC techniques, termed cost-reference particle filters (CRPFs), to tracking of a high-speed maneuvering target. The new CRPF methodology drops all probabilistic assumptions (i.e., prior probabilities, knowledge of noise distributions and likelihood functions) that are common to conventional particle filters and, as a consequence, leads to practically more robust algorithms. The advantage of the proposed CRPF over the standard SMC filter in the context of maneuvering target tracking is illustrated through computer simulations.