This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli
(GLMB) filter by combining the prediction and update into a single step. In contrast to an …

This article presents the Labeled Random Finite Set (LRFS) framework for multi-object
systems–systems in which the number of objects and their states are unknown and vary …

Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It
improves the accuracy and stability of target tracking by fusing multiple local sensor …

This paper proposes an efficient implementation of the multi-sensor generalized labeled
multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation …

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 …

State-space models in which the system state is a finite set-called the multi-object state-have
generated considerable interest in recent years. Smoothing for state-space models provides …

In this paper, we propose a distributed multiobject tracking algorithm through the use of multi-
Bernoulli (MB) filter based on generalized covariance intersection (G-CI). Our analyses …

Most tracking algorithms in the literature assume that the targets always generate
measurements independently of each other, ie, the sensor is assumed to have infinite …

This paper addresses multi-agent multi-object tracking with labeled random finite sets via
Generalized Covariance Intersection (GCI) fusion. While standard GCI fusion of Labeled …

The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that
alleviates the limitations of the Poisson family in dynamic Bayesian inference of point …