Multisensor and Multitarget Tracking Based on Generalized Covariance Intersection Rule

Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because traditional GCI only has good performance targets each sensor’s FoV, and outside range would be lost, paper redivides Gaussian components according FoV distinguishes inside intersection. sensitive inconsistency between different sensors. For region, best match labels found by minimizing index, then performed. Finally, feasibility effectiveness proposed method are verified simulation, its robustness proved. The obviously superior local sensor algorithm.