Improved probabilistic multi-hypothesis tracker via the Poisson point process

Optimal data association is the main task of multi-target tracking due to the similarity of the tracker’s filtering parts.Traditional Multi-target tracking methods pick up the optimal data association from all possible associations that account for the complexity exponentially increasing with the number of targets and limiting the maximum number of targets which can be stably tracked.This paper proposes an efficient and accurate method where the measurement points raised by targets and clutter are modeled as the Poisson point process and the expectation maximisation algorithm is utilized to estimate the target states recursively.Independent data association and mixing probability decrease the computational complexity.Furthermore,Doppler information refers to the fact that the target feature has been used in association and filtering stage to improve tracking performance without adding complexity.The experiment with simulation data show that the performance of the proposed method is better than that of the traditional method with a shorter operation time.