Joint RSS/AOA-Based Energy-Efficient 3-D Radio Localization with UAV

Reinforcement learning based radio localization with unmanned aerial vehicle (UAV) that optimizes the locations to measure the received signal strength (RSS) of the radio device is inaccurate with high energy consumption for 3-D radio localization due to the varying shadowing effect. In this paper, we propose a 3-D radio localization scheme that exploits the angle of arrival (AOA) estimated by the uniform planar array besides the RSS. The weight and trajectory are optimized based on the UAV position, RSS measurement, angle estimations and battery level to enhance the accuracy with reduced energy consumption. The threshold of the localization errors is formulated as the safety criterion to avoid choosing the waypoint with large ranging and AOA estimation errors. The Cramér-Rao lower bound is derived to show the impact of the total number of waypoints, snapshots and the antenna configuration. Simulation results based on 5 waypoints to localize the radio device with transmit power 100 mW show that the proposed scheme decreases the localization error and energy consumption over the benchmarks.