Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source
information fusion problems, and thereby, their corresponding fusion problems have …

Multisensor fusion and consensus filtering are two fascinating subjects in the research of
sensor networks. In this survey, we will cover both classic results and recent advances …

The Internet of Things (IoT) is set to become one of the key technological developments of
our times provided we are able to realize its full potential. The number of objects connected …

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 …

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 …

Recently, solving ordinal classification problems using machine learning and deep learning
techniques has acquired important attention. There are many real-world problems in …

We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing
problem under linear and log-linear combination rules. We show that under both …

We consider the challenging problem of tracking multiple objects using a distributed network
of sensors. In the practical setting of nodes with limited field of views (FoVs), computing …

A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor
nodes and fuse the filtered distributions algorithmically using their exponential mixture …

Distributed data fusion (DDF) is the process whereby a group of agents sense their local
environment, communicate with other agents, and collectively try to infer knowledge about a …