Near Neighbor Cheap JPDA IMM based on amplitude information

Abstract

A Near Neighbor Cheap JPDA (NNCJPDA) Interacting Multiple Model (IMM) algorithm based on amplitude information (AI) is proposed to improve the performance of multi-target tracking under conditions of low signal to noise ratio (SNR) or high false alarm rate. In this algorithm, the association likelihood of NNCJPDA is combined with the likelihood ratio of amplitude. Under conditions of low SNR or high false alarm rate, the amplitude information can be used to improve the accuracy of tracking and reduce the amount of computation. Simulation results demonstrate that the proposed algorithm improves the performance of target tracking and computational efficiency, and ensures the astringency of the system.