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 …

In this paper, we derive a multisensor multi-Bernoulli (MS-MeMBer) filter for multitarget
tracking. Measurements from multiple sensors are employed by the proposed filter to update …

The single-sensor probability hypothesis density (PHD) and cardinalized probability
hypothesis density (CPHD) filters have been developed in the literature using the random …

The goal of maritime situational awareness (MSA) is to provide a seamless wide‐area
operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a …

Existing multisensor multitarget tracking solutions have complexities that grow super-
exponentially wrt the number of sensors. In this letter, we propose a novel algorithm for …

The Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-
target tracking problems in a Bayesian framework, but the extension to the multi-sensor case …

The single‐sensor Poisson multi‐Bernoulli (MB) mixture (PMBM) filter has been developed
for multi‐target tracking (MTT). However, there is a lack of research on the multi‐sensor (MS) …

Multiple target tracking concerns the estimation of an unknown and time-varying number of
objects (targets) as they dynamically evolve over time from a sequence of measurements …

Random finite set (RFS) based filters such as the cardinalized probability hypothesis density
(CPHD) filter have been successfully applied to the problem of single sensor multitarget …

Compared with conventional monostatic or bistatic radar, multistatic radar has wider
coverage, better performance of localization and higher tracking accuracy. However, the …