Abstract
Unmanned aerial vehicle (UAV) is regarded as a powerful tool to expand the existing ground wireless network into aerial space. Since high mobility is an essential characteristic for UAV, it is important to carry out an accurate, real-time, and high-precision localization in terms of safe operation and communication link maintenance. The cellular network-based localization technology has provided UAV a solution with both high coverage and seamless connection. However, the complex channel environment between the UAV and terrestrial base station (BS) would have weakened the localization performance. To solve this problem, a two-stage channel adaptive algorithm for cellular-connected UAV has been proposed. The first stage of the algorithm is to revise the observation error introduced by the complex channel environment using the model of DDPG. The second stage is to locate the UAV position with TDOA algorithm using the revised observation values. Simulation results have demonstrated that the proposed algorithm can achieve the channel adaptive effect by revising the observation errors and improve location performance greatly, especially for UAVs at a relative lower altitude.
Highlights
With the large-scale deployment and application of the fifth generation (5G) cellular system, researchers start to focus their studies on the sixth generation (6G) mobile communication network
Simulation Parameters. e 3GPP research project has studied three cellular-connected Unmanned aerial vehicle (UAV) scenarios, namely, urban-macro with aerial vehicles (UMa-AV), urban-micro with aerial vehicles (UMi-AV), and rural-macro with aerial vehicles (RMa-AV) [7]. e eNodeB antennas mounted above the rooftop levels of surrounding buildings in urban environment are UMa-AV scenarios
UMi-AV represents scenarios where eNodeB antennas are mounted below rooftop. eNodeB antennas mounted on top of towers of larger cells in rural environment are represented by RMaAV [31]
Summary
With the large-scale deployment and application of the fifth generation (5G) cellular system, researchers start to focus their studies on the sixth generation (6G) mobile communication network. In order to fulfill the innovative objective in wireless communications for the coming 6G communication systems and provide an ondemand connectivity from the sky, new subjects such as satellites, high- and low-altitude platforms, drones, aircrafts, and airships are being included to take the role as aerial base stations. According to [3], GNSS (Global Navigation Satellite System), INS (Inertial Navigation System), and visual-based navigation are three technologies used by UAV localization. GNSS is the most widely used one for its global coverage; the satellite signal is sensitive to obstacles and blockings, leading to accuracy drops in complex environments like city centers. Visual-based localization demands large amount of image processing, which requires high computing capability and increases the system complexity
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