It caters to the inherent distributed feature of sensor networks and does not need measurements to be transmitted among sensors for target states predictions.
Aiming to address such challenges, this paper proposes a dis- tributed GP-based tracking approach able to learn the kernel hyperparameters in an online manner, ...
Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs).
This article proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions.
This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian ...
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This paper studies the target tracking problem over wireless sensor networks (WSNs). While most existing works on this problem focus on the single-target ...
—We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing , tracking, and forecasting (STF), for a class ...
Dec 7, 2023 · This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and ...
Jul 7, 2022 · A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks. ABSTRACT. Tracking manoeuvring targets often ...
A data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions and shows that the ...