Spawned Target Tracking Algorithm Based on GLMB Model in RFS Theory

摘要

This paper proposes a spawned targets tracking algorithm—STT-δ-GLMB—by extending the δ-GLMB algorithm. In the course of the research, we found that for the original δ-GLMB algorithm, performance of the spawned target tracking is seriously insufficient in the environment of the low signal-to-noise ratio. For this problem, the biggest difficulty is spawned target birth detection, and we need to model spawned target. Therefore, this paper uses the characteristics, position and velocity of the spawned target, and multiple frames of measurements to detect the spawned target. By referring to parent target state arguments, we model spawned target based on δ-GLMB. Simulation results show that under low detection rate and strong clutters, the proposed algorithm can accurately estimate spawned targets, and the performance is significantly better than the original δ-GLMB algorithm.