MmWave radar features extraction of drones for machine learning classification

With the progressive reduction of cost, in the market it is possible to find a very large assortment of Unmanned Aerial Vehicles (UAV) that are used in general for non-warlike activities. Unfortunately, it may happen that malicious subjects use these objects to cause damage or inconvenience, then the availability of solutions to predict these situations can be crucial for alerting the population and saving lives. In this work, we present a technique to identify drones from their micro-Doppler features, by analyzing their variations during the flight. The characterization of the features and how they evolve in time is useful to predict dangerous situations and classify the drone type, with the help of Machine Learning techniques.