RLS-assisted cost reference particle filtering

Cost-reference particle filtering (CRPF) allows for tracking of nonlinear dynamic states without a prior knowledge of the probability distributions of the noises in the state-space representation of the system. In this paper we consider a setup where the system unknowns consist of linear and nonlinear states. We propose an efficient scheme for estimation of the states by combining CRPF with the recursive least square (RLS) algorithm. We applied the method to the problem of target tracking using biased bearing measurements. Simulation results show a very accurate performance of the proposed approach.