An interacting multiple models probabilistic data association algorithm for maneuvering target tracking in clutter

摘要

To solve the problem of tracking maneuvering target in the presence of clutter, the debiased converted measurement based interacting multiple model (IMMDCM) estimator in combination with the probabilistic data association (PDA) technique is proposed for airborne target tracking. Under the architecture of the proposed algorithm, the IMM deals with the model switching, the debiased converted measurement filter accounts for non-linearity in the dynamic system models, while the PDA handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMEKF based PDA algorithm.