Radar-Based Autonomous Identification of Propellers Type for Malicious Drone Detection

Commercial drones, initially developed for military applications, have become widely used in various civil sectors. Although potentially very useful, however, drones may also pose security concerns. These devices can be used for mali-cious purposes like attacks on civilians or terrorism. Therefore, detection methods exploiting sundry technologies (e.g., video surveillance, photoelectric, etc.) to automatically identify drones have become compelling assets. Various solutions exist for drone detection and tracking, but accurate identification still remains an open issue, especially when Critical Infrastructures (CIs) are involved. To this end, this paper proposes a novel identification system, based on a linear Frequency Modulated Continuous Wave (FMCW) Multiple Input Multiple Output (MIMO) Radar sensor, to autonomously detect the type of propellers installed on drones and fulfil security issues for CIs. Specifically, supposing that a CI may deploy a swarm of drones of the same model and make, all equipped with propellers of the same material and shape, a blacklisting approach is employed, where all drones not equipped with those specific propellers are deemed as potentially malicious. Preliminary test results proved that such a task is feasible by leveraging the capabilities of the Radar sensor to extract the vibration information of the drone chassis. We achieve identification with a 500 ms long vibration signal, on which the Discrete Fourier Transform (DFT) is applied, and then by analysing the values of displacement and frequency of the first DFT peak.