An explicit track continuity algorithm for the GM-PHD filter
Yiyue Gao, Ni Fang, Defu Jiang, Wei Fu, Su Guo · 2019
In this study, an explicit track continuity algorithm is proposed for multi-target tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. In this approach, the Gaussian components are classified and labeled, and multi-target state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates. Currently, trajectory maintenance and multi-target state extraction in the GM-PHD filter have not been effectively integrated. This paper realizes the integration of trajectory maintenance with state extraction in the GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity and obtain slightly lower accuracy than that of the generalized labeled multi-Bernoulli filter while maintaining the low computational cost of the GM-PHD filter.