Multiple radio transmitter localization via UAV-based mapping

The widespread use of unmanned aerial vehicles (UAVs) has opened a novel perspective on spectrum awareness and localization because of their ability to overcome spatial limits and the burden of dedicated infrastructure. In such scenarios, an underinvestigated problem is the simultaneous localization of multiple non-collaborative primary users (PUs) of the spectrum, whose number, transmit power, activity pattern, and signal structure are unknown. This work proposes a framework for multiple PU localization based on the received power measured by an antenna array mounted on a UAV. A score map is firstly constructed based on the measured power. Then two algorithms, k-means clustering and weighted centroid (KCWC), and Gaussian mixture model fitting (GMMF) are applied to the score map to estimate the number and the positions of the PUs. The performance is evaluated in terms of optimal subpattern assignment (OSPA) distance, compared with a genie-aided (GA) localization algorithm capable of separating the signals emitted by the PUs. Despite the blind nature of the method proposed, numerical results exhibit very good localization accuracy, with an OSPA distance below 6 m in large, line-of-sight (LOS)-dominated, outdoor scenarios with five PUs, even in the presence of channel impairments and UAV position and heading uncertainties.