Adaptive interacting multiple model tracking of manuevering targets

In this paper we investigate adaptive interacting multiple model (AIMM) tracking techniques. Here we compare two existing IMM approaches and propose a new novel technique. Our algorithm is based on the interacting multiple model (IMM) tracking method with the addition of an adaptive acceleration model to track behavior that falls in between the fixed model dynamics. In this research, we found that the adaptive model matches more closely the true system dynamics when the target kinematics lie in between the fixed models thus improving the overall performance of the tracking system. Also, we found that our new AIMM outperforms the classical IMM as well as the existing adaptive approaches with reduced computational complexity.