Li Suqi

TL;DR: This letter considers the distributed multi-target tracking through the use of multi-Bernoulli based on generalized Covariance Intersection (G-CI) and approximate the fused posterior as an unlabeled version of δ-generalized labelled multi- Bernoulli (δ-GLMB) distribution, referred to as δ.

TL;DR: This paper considers the problem of the distributed fusion of multiobject posteriors in the labeled random finite set filtering framework using a generalized covariance intersection (GCI) method and proposes a novel and general solution that is robust to label inconsistencies between sensors.

TL;DR: This paper presents a novel approach for the GCI fusion of LMO densities that is both robust to label inconsistencies and computationally efficient and shows how the label matching problem can be formulated as a linear assignment problem of finite length.

TL;DR: A novel distributed HBRS filter is developed and its effectiveness is tested on a case study concerning wide-area monitoring of a power network.

TL;DR: This letter considers the distributed multi-target tracking through the use of multi-Bernoulli based on generalized Covariance Intersection (G-CI) and approximate the fused posterior as an unlabeled version of δ-generalized labelled multi- Bernoulli (δ-GLMB) distribution, referred to as δ.