In this study, we provide an overview of recent advances in multisensor multitarget tracking
based on the random finite set (RFS) approach. The fusion that plays a fundamental role in …

Fusing probabilistic information is a fundamental task in signal and data processing with
relevance to many fields of technology and science. In this work, we investigate the fusion of …

A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB)
filter is proposed. The algorithm is capable of tracking a very large, unknown and time …

Networked radar systems have been demonstrated to offer enhanced target tracking
capabilities. An effective radar resource allocation strategy can efficiently optimize system …

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 …

We propose a novel consensus notion, called “partial consensus,” for distributed Gaussian
mixture probability hypothesis density fusion based on a decentralized sensor network, in …

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 …

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

A distributed multiple-input multiple-output (MIMO) radar system is capable of tracking
multiple targets under the “defocused transmit-focused receive”(DTFR) operating mode, in …