PARTICLE FILTERING FOR MANEUVERING TARGET TRACKING IN CLUTTER

In this paper, we introduce the mixed particle filtering PDA (MPF-PDA) algorithm, an efficient variant on the PF for nonlinear maneuvering target tracking in clutter. Each particle samples a discrete mode and approximates the continuous state by a Gaussian distribution which is updated by a combination of the Unscented Kalman filter (UKF) and PDA. The discrete mode is estimated by an improved PF combined with PDA. The posterior distribution of the target state is approximated with a mixture of Gaussians. Monte Carlo simulations show performance improvement of the proposed algorithm over traditional bootstrap particle filtering, and the superiority for large clutter densities.