A multiple-model probability hypothesis density filter for tracking maneuvering targets

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the target number varies, in which case the dimensionality of the state space itself becomes a discrete random variable. The Probability Hypothesis Density (PHD) filter, which propagates only the first-order statistical moment (or the PHD) of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems with varying number of targets. The integral of PHD in any region of the state space gives the expected number of targets in that region. With maneuvering targets, detecting and tracking the changes in the target motion model also become important, but current PHD implementations do not provide a mechanism for handling this. The target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-conditioned estimates in a manner similar to the one used in the Interacting Multiple Model (IMM) estimator. In this paper a multiple model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using Sequential Monte Carlo methods, is proposed. The resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over single-model PHD filters.