As the number of drones, or Unmanned Aerial Vehicles (UAVs), rapidly rises, their detection becomes a very important task in outdoor surveillance, to prevent accidents or inappropriate use. With this goal it is also important to collect as much information as possible about the drone and this can be obtained with radar systems, exploiting the micro-Doppler signature of the drone: preliminary information can be obtained by using machine learning (ML) classification techniques but also by measuring the rotational speed of the propellers. The proposed approach described in this work can provide a better understanding of the detected UAVs which can be used to improve the safety of outdoor spaces.
Drone classification using mmWave micro-Doppler radar measurements
Ciattaglia G.;Senigagliesi L.;Iadarola G.;Spinsante S.;Gambi E.
2022-01-01
Abstract
As the number of drones, or Unmanned Aerial Vehicles (UAVs), rapidly rises, their detection becomes a very important task in outdoor surveillance, to prevent accidents or inappropriate use. With this goal it is also important to collect as much information as possible about the drone and this can be obtained with radar systems, exploiting the micro-Doppler signature of the drone: preliminary information can be obtained by using machine learning (ML) classification techniques but also by measuring the rotational speed of the propellers. The proposed approach described in this work can provide a better understanding of the detected UAVs which can be used to improve the safety of outdoor spaces.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
