Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

Abstract

Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature.