Multi-sensor multi-target tracking method based on posterior track estimation

Abstract

The invention discloses a multi-sensor multi-target tracking method based on posterior track estimation, and the method comprises the steps: obtaining the posterior track probability hypothesis density of each sensor and a corresponding posterior track estimation set through a track probability hypothesis density filtering method, and constructing an association cost matrix between different sensors; dividing posterior tracks in the posterior track estimation sets of different sensors into associated tracks and non-associated tracks; fusing the probability hypothesis densities of the associated tracks among different sensors to obtain a fused associated track probability hypothesis density; error tracks in the non-associated tracks among different sensors are removed, the probability hypothesis density of the non-associated tracks after the error tracks are removed is fused with the probability hypothesis density of the fused associated tracks, and the probability hypothesis density of the fused tracks among the different sensors is obtained; and according to the fusion track probability hypothesis density among different sensors, extracting to obtain a multi-sensor estimated track state. According to the invention, the communication load is obviously reduced, and the problem of missing detection in GCI fusion is solved.

Description

technical field [0001] The invention belongs to the technical field of target tracking, in particular to a multi-sensor multi-target tracking method based on a posteriori track estimation. Background technique [0002] Distributed multi-sensor multi-target tracking (DMMT) refers to the comprehensive use of the noisy observation information provided by multi-sensors, through proper fusion processing, to remove the false and preserve the truth, learn from each other's strengths and complement each other, and obtain a more comprehensive and accurate target navigation than a single sensor. Tracking state is the key technology of military-civilian reconnaissance and surveillance system, which mainly faces two major challenges: multi-target tracking and distributed information fusion. [0003] The most typical multi-target tracking methods include multi-hypothesis tracking, joint probability data association and random finite set methods. From a Bayesian point of view, finite set...

AI Technical Summary

Problems solved by technology

However, the direct combined application will have the following three major problems: first, the communication load required to transfer the probability assumption density of the posterior track probability between nodes is too large; second, simple GCI fusion is easy to produce counter-intuitive wrong tracks; Due to the inconsistency of node potential distribution, the target missed detection, etc.

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