Current statistical model probability hypothesis density filter for multiple maneuvering targets tracking

Abstract

The probability hypothesis density (PHD) filter, which propagates only the first moment (or PHD) instead of the full target posterior, has been shown to be a computationally efficient solution to multi-target tracking problems. Incorporating the current statistical model that is effective in dealing with the maneuvering motions, this paper proposes a current statistical model PHD (CSMPHD) filter for multiple maneuvering targets tracking. This proposed filter approximates the PHD by a set of weighted random samples propagated over time based on the current statistical model using sequential Monte Carlo (SMC) methods. Simulation results demonstrate that the proposed filter shows similar performances with the multiple-model PHD (MMPHD) filter, but it avoids the difficulty of model selection for maneuvering targets and has faster processing rate.