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.
Highlights
Unmanned aerial vehicles (UAVs) are increasingly demonstrating their potential for use in various military and civilian applications
We propose a 3D UAV localization algorithm through multi-lateration that is based on 5G received signal strength index (RSSI) measurements from 4 base stations
We provide a comparative analysis with a benchmark solution to the UAV localization problem using cellular signals proposed in the literature based on cellular carrier phase measurements and weighted non-linear least squares estimation
Summary
Unmanned aerial vehicles (UAVs) are increasingly demonstrating their potential for use in various military and civilian applications. The presented techniques either mostly focus on the localization of the agent in the 2-D space assuming the height of the mobile device is known yielding low accuracy when extended to 3-D or require extensive knowledge of the environment during the training phase to build a fine-grained accurate fingerprint map. Such proposed approaches and data collection processes would be infeasible and unscalable in large 3-D outdoor geographic areas and unknown environments
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