Drone detection by acoustic signature identification

In the last years, the ductility and easiness of usage of unmanned aerial vehicles (UAV) and their affordable cost have increased the drones use by industry and private users. However, drones carry the potential of many illegal activities from smuggling illicit material, unauthorized reconnaissance and surveillance of targets and individuals, to electronic and kinetic attacks in the worse threatening scenarios. As a consequence, it has become important to develop effective and affordable coun- termeasures to report of a drone flying over critical areas. In this context, our research chooses different short term parametrization in time and frequency domain of environmental audio data to develop a machine learning based UAV warning system which employs the support vector machines to understand and recognize the drone audio fingerprint. Preliminary experimental results have shown the effectiveness of the proposed approach.