Multi-sensor multi-target tracking based on distributed PMHT
In the field of target tracking,probability multiple hypothesis tracking(PMHT)algorithm,as a batch processing algorithm,has much less computation than the traditional multiple hypothesis tracking algorithm.Currently,the application of PMHT algorithm is limited by centralized processing.On the basis of the traditional algorithm,this study firstly derives the algorithm likelihood under sensor network to obtain the post-correlation parameter under multi-sensor algorithm,followed by hybrid consensus based on the consensus processing strategy,and finally the posteriori estimation of the target parameters is accomplished by using Kalman filtering.This study enables the PMHT algorithm to be applied to the fully distributed sensor network without fusion centers.The experimental results show that under different clutter densities,the distributed PMHT has more than 90％improvement in tracking error compared to the single-sensor algorithm.Distributed PMHT has close tracking performance and faster computation compared to centralized algorithms.
multi-target trackingprobability multiple hypothesis tracking(PMHT)consensuscentralized state estimationdistributed state estimation
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