This article focusses on the fusion of information from various automotive sensors like radar,
video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from …

In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty,
clutter and detection. Knowledge of parameters such as clutter rate and detection profile are …

Precise localization and tracking of moving objects is of great interest for a variety of
emerging applications including the Internet-of-Things (IoT). The localization and tracking …

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form
solution to the probability hypothesis density (PHD) recursion, which was proposed for …

This paper proposes a novel particle labeling (termed asdyeing ‚) method for track continuity
for the sequential Monte Carlo (SMC) implementation of the probability hypothesis density …

This overview paper describes the particle methods developed for the implementation of the
class of Bayes filters formulated using the random finite set formalism. It is primarily intended …

This paper proposes a filter for joint detection and tracking of a single target using
measurements from multiple sensors under the presence of detection uncertainty and …

THE multi-object filtering problem is a logical and fundamental generalization of the
ubiquitous single-object vector filtering problem. Multi-object filtering essentially concerns …

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 …

An algorithm that is capable not only of tracking multiple targets but also of “track
management”—meaning that it does not need to know the number of targets as a user input …