The field of prognostics has attracted significant interest from the research community in
recent times. Prognostics enables the prediction of failures in machines resulting in benefits …

In this paper, we propose using Gaussian processes to track an extended object or group of
objects, that generates multiple measurements at each scan. The shape and the kinematics …

Two approaches for on-line Gaussian process regression with low computational and
memory demands are proposed. The first approach assumes known hyperparameters and …

With a principled representation of uncertainty and closed form posterior updates, Gaussian
processes (GPs) are a natural choice for online decision making. However, Gaussian …

There is a big trend nowadays to use event-triggered proximity report for indoor positioning.
This paper presents a generic received-signal-strength (RSS) threshold optimization …

This paper addresses the problem of tracking multiple extended targets in three-dimensional
space. We propose the Gaussian process Gaussian mixture probability hypothesis density …

We propose a distributed recursive Gaussian process (drGP) regression framework for
building received-signal-strength (RSS) map. The proposed framework adopts independent …

In this study, we consider the challenging task of tracking dynamic 3D objects with unknown
shapes by using sparse point cloud measurements gathered from the surface of the objects …

There has been a growing interest in using non-parametric regression methods like
Gaussian Process (GP) regression for system identification. GP regression does traditionally …

Modern advanced driver assistance systems (ADAS) and automated driving functions for
automobiles rely on an accurate model of the environment. To this end, the exploitation of …