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 …

This paper addresses the problem of distributed multitarget detection and tracking based on
the linear arithmetic average (AA) fusion. We first analyze the conservativeness and Fréchet …

From the celebrated Gaussian mixture, model averaging estimators to the cutting-edge multi-
Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible …

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 …

The linear opinion pool (LinOP) provides a potential solution to the problem of information
fusion. However, the LinOP cannot be directly applied to multi-object fusion since the …

In this article, we propose a multisensor cardinalized probability density hypothesis (CPHD)
filter for tracking an unknown number of targets that may maneuver over time by using a …

We propose a particle-based distributed PHD filter for tracking the states of an unknown,
time-varying number of targets. To reduce communication, the local PHD filters at …

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 propose a computationally efficient, the first so far, multi-sensor extension of the Poisson
multi-Bernoulli mixture (PMBM) filter that accommodates both centralized and distributed …