A Novel Interacting Multiple Model Parti- Cle Filter for Maneuvering Target Track- Ing in Clutter

In this paper, a novel interactive multiple model particle filter (IMMPF) is developed after a Bayesian estimator for maneuvering target tracking in clutter is derived theoretically. In this new algorithm, base state estimation and modal state estimation are completely separated to control the number of particles in each maneuvering mode. Only continuous-valued particles are used to numerically implement the procedure of Bayesian base state estimation, whereas modal state is estimated analytically without dependence on the number of particles. Density mixing is performed by aggregation of the total particles and mixing associated weights. To prevent the exponentially growing number of particles with the time, a resampling step is included following the interaction step. Through MC simulations, the new IMMPF has been tested and shown to provide reliable performance improvements with different sample sizes and under various clutter conditions.