Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive
estimation of dynamic systems, recently emerged from the random set theoretical framework …

We review some advances of the particle filtering (PF) algorithm that have been achieved in
the last decade in the context of target tracking, with regard to either a single target or …

This paper proposes a generalization of the multi-Bernoulli filter called the labeled multi-
Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not …

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets
and their states or trajectories from noisy sensor measurements. MTT has a long history …

The paper addresses distributed multitarget tracking over a network of heterogeneous and
geographically dispersed nodes with sensing, communication and processing capabilities …

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular
networks when the base station and vehicles are equipped with large antenna arrays …

The paper addresses the problem of distributed multi-target tracking (MTT) in a network of
sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are …

In this paper, we consider the problem of Distributed Multi-sensor Multi-target Tracking
(DMMT) for networked fusion systems. Many existing approaches for DMMT use multiple …

This work presents the current state-of-the-art in techniques for tracking a number of objects
moving in a coordinated and interacting fashion. Groups are structured objects …

In this paper, we propose a multi-level cooperative fusion approach to address the online
multiple human tracking problem in a Gaussian mixture probability hypothesis density (GM …