Aiming at the problems that the distributed multi-sensor multi-target tracking(DMMT)method based on probability hypothesis density(PHD)cannot form a track,the computational complexity is high,and miss-detections.In this paper,a DMMT method is developed with trajectory PHD posterior estimation.Firstly,the similarity measure matrix between the estimated trajectories of each node is constructed,and the optimal trajectory matching is achieved by using the Hungarian algorithm.Secondly,the covariance intersection rule is used to achieve parallel fusion for the associated trajectories.Finally,a robust DMMT method is derived based on probabilistic generative functionals.Simulation experiments verify the advantages of the proposed algorithm in terms of multi-target state estimation accuracy,computational efficiency and real-timeliness.
关键词
航迹概率假设密度/最优航迹匹配/广义协方差逆/概率生成泛函
Key words
trajectory probability hypothesis density/optimal trajectories matching/generalized covariance intersection/probabilistic generative functionals
维普
万方数据