Wi-Fi-based Real-Time UAV Localization: a Comparative Analysis between RSSI-based and FTM-based Approaches

Wi-Fi connectivity for localization purposes has been used for several years in the Internet of Things (IoT) context, where the (general) static nature of IoT devices allows to approximately localize them in known environments with low effort and implementation costs. While the accuracy of Wi-Fi localization for IoT applications can be considered as acceptable, the adoption of Wi-Fi-based localization for (a highly mobile) UAV has received limited attention. In this paper, a low cost and complexity system architecture is proposed and exploited to perform a comparative analysis between two Wi-Fi-based localization approaches: the traditional Received Signal Strength Indicator (RSSI) ranging and the more recent Fine Time Measurement (FTM), based on the IEEE 802.11mc amendment. Our goal is to estimate and compare the efficacy of the proposed system for real-time positioning of a static or moving UAV, evaluating the impact of different filtering solutions on the localization accuracy. The obtained results show that FTM-based localization is more accurate, reducing the positioning error by 37% with respect to the RSSI-based positioning approach. Our results also confirm the better overall performance of the FTM-based solution for low cost localization applications, discussing its limitations, scalability, and advantages as a viable backup positioning solution in (weak or denied) GNSS-based environments and scenarios.