Constrained Multiple Model Probability Hypothesis Density Filter for Maneuvering Ground Target Tracking

There are many constraints in the motion of a ground target, for example, geographic constraints. So it is complicated to track a ground target. However, meanwhile, geographic constraints are a sort of information. How to apply these information properly is a worthy problem to study. For maneuvering ground targets, constrained multiple model Gaussian mixture probability hypothesis density (CMM-GMPHD) filter is proposed in this paper. Model conditioned distribution and model probability are used in the proposed CMM-GMPHD filter. In the proposed method, the Gaussian component in the GM-PHD filter is estimated by multiple model method, and the final results of the Gaussian components in PHD of maneuvering ground targets are the fusion of multiple model estimations. In addition, the road information is described as equality constraints and then it is used to correct the estimated state in the method. The simulation results indicate that the proposed algorithm can track the maneuvering ground targets steadily in the environment of clutter.