Abstract

This paper explores the experimental localization of single and multiple ground RF transmitters using both traditional localization and machine learning algorithms. For the localization of a single transmitter, the setup is evaluated in two unlicensed frequency bands with and without interference. A threshold approach is proposed to improve accuracy in the presence of interference. To localize multiple transmitters, the RSSI data are divided into clusters by a k-means clustering algorithm and fed into a localization algorithm. These experimental results are preceded by an analysis phase where the UAV flight path and data collection are simulated using the QuaDRiGa channel model.