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 …

This article proposes a solution of distributed multiple resolvable group targets tracking
(DMRGTT) by exploring the labeled multi-Bernoulli (LMB) filter and distributed fusion rules …

In this chapter, we provide an overview of cutting-edge approaches and remaining
challenges in multisensor multitarget information fusion based on the random finite set …

In this article, the interest is on the fusion of labeled random finite set (LRFS) densities
computed by sensors having different fields of view (FoVs). In order to deal with different …

This paper addresses distributed multi-target tracking over a network of sensors having
different fields-of-view (FoVs). Specifically, a cardinalized probability hypothesis density …

The focus of this article is to tackle the challenging task of unresolved-group object (UO)
tracking by exploiting the Poisson multi-Bernoulli mixture (PMBM) filter, named UO-PMBM …

This article proposes a joint estimate approach in terms of target states and target cardinality
as well as detection probability based on the Poisson multi-Bernoulli (PMB) filter. For the …

The nodes in a decentralized network have equal status and can communicate with their
neighbors. Considering the target tracking problem for a strongly connected decentralized …

Most multi-target movements are nonlinear in the process of movement. The common multi-
target tracking filtering methods directly act on the multi-target tracking system of nonlinear …

In response to the issue of different perspectives affecting fusion accuracy in distributed
multisensor multitarget tracking (DMMT), this study investigates the fusion strategies for …