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

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 …

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 …

This paper presents a new solution for multi-target tracking over a network of sensors with
limited spatial coverage. The proposed solution is based on the centralized data fusion …

This paper proposes a computationally efficient algorithm for distributed fusion in a sensor
network in which multi-Bernoulli (MB) filters are locally running in every sensor node for …

This paper presents a new solution for statistical fusion of multi-sensor information acquired
from different fields of view, in a centralized sensor network. The focus is on applications that …

This paper presents a new sensor management method for multitarget filtering, that is
designed based on maximizing a measure of confidence in accuracy of the multitarget state …

Sensor management in multi-object stochastic systems is a theoretically and
computationally challenging problem. This paper presents a new approach to the multi …

A new sensor-selection solution within a multi-Bernoulli-based multi-target tracking
framework is presented. The proposed method is especially designed for the general multi …

This paper presents a novel method for track-to-track fusion to integrate multiple-view sensor
data in a centralized sensor network. The proposed method overcomes the drawbacks of the …