Cooperative Target Tracking by Multiagent Camera Sensor Networks via Gaussian Process

Abstract

In this paper, we present learning-based robust cooperative control for camera sensor networks. The dynamics of each camera agent with the pan and tilt mechanism is modeled by the Euler-Lagrange equation. In the target tracking problem, the modeling errors caused by high nonlinearity of camera system dynamics and changes in the tracking environment may incur significant deterioration of tracking performance. To address this, we propose a distributed control method based on the Gaussian process regression to compensate for the model uncertainty of the dynamics of the camera sensor network. We consider the error dynamics of the multiagent Euler-Lagrange system to evaluate the tracking errors of the camera agents. We show that the ultimate boundedness of the error dynamics is achieved by the proposed learning-based method. A numerical example confirms that the proposed control law enables each camera agent to track the targets with a bounded tracking error in the presence of model uncertainty.