Maneuvering target tracking based on a random motion model and integrated random interacting multiple model filtering

Abstract

Traditional maneuvering target tracking algorithms assume that the target motion model is either fixed or limited in number. For high-speed and highly maneuvering targets, the tracker's performance degrades rapidly when the model set fails to adequately encompass the maneuvering mode or when there is a substantial deviation. Therefore, we propose a novel maneuvering target tracking method based on a random motion model. This algorithm employs a random model to describe the target maneuver, which is more widely applicable than traditional algorithms and remains more stable when the target maneuver is not covered by the model set. Additionally, in cases where the model set of the Interacting Multiple Model algorithm (IMM) does not align with the actual maneuvering state, the new method exhibits a smaller tracking error compared to IMM and shows no divergence trend. Finally, we combine IMM and the random motion model to propose an Integrated Random Interacting Multiple Model algorithm (IRIMM). The performance of the IRIMM algorithm closely matches that of IMM when provided with a perfectly accurate model set and significantly improves tracking effectiveness and stability when the model is incorrect.