UAV tracking of mobile target in occluded, cluttered and GPS-denied environments

This paper presents a novel approach for mobile target tracking using a UAV flying in a cluttered and GPS-denied environment that occludes the target partially and momentarily. The problem is modelled as a sequential decision process under uncertainty using the Partially Observable Markov Decision Process framework (POMDP). The motion of the UAV is planned as sequences of high-level actions in order to optimise a multi-objective mission. A modular system executes the motion planning. The UAV and target dynamics models are included in the POMDP transition function as a black-box simulator. The target is detected using a front facing camera onboard the UAV. A model of the projection of the target and obstacles into the image plane of the camera is presented and used in the POMDP transition function. This model enables the POMDP solver to include the occlusions in the Field of View (FOV) of the camera caused by the obstacles in the scenario. Results in simulation indicate that the developed system is able to guide the UAV towards locations in which the target tracking duration is maximised whilst avoiding collisions with obstacles. This research has the potential to be used below the forest canopy, in fault or GPS-denied environments, underground mines and urban canyons