We review some advances of the particle filtering (PF) algorithm that have been achieved in
the last decade in the context of target tracking, with regard to either a single target or …

We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multitarget
tracking with the standard point target measurements without using probability generating …

The frequent occurrence of global climate change and natural disasters highlights the
importance of precision agricultural monitoring, yield forecasting, and early warning …

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets
and their states or trajectories from noisy sensor measurements. MTT has a long history …

Multi-frame track-before-detect (MF-TBD) is a model-based batch processing method.
Assuming a particular model for the evolution of target states (eg a constant velocity model) …

This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target
tracking: one to estimate the set of alive trajectories at each time step and another to …

In this article, we develop a multiframe detection architecture relying on the generalized
likelihood ratio test (GLRT) to address complicated multitarget detection and tracking in …

We propose a solution of the multiple target tracking (MTT) problem based on sets of
trajectories and the random finite set framework. A full Bayesian approach to MTT should …

This work considers the underwater tracking of an unknown and time-varying number of
targets, ie, acoustic emitters, using passive array sonar systems. This problem becomes …

This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD
(CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and …