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.

MmWave radar features extraction of drones for machine learning classification

Ciattaglia G.
;
Spinsante S.;Gambi E.
2021-01-01

Abstract

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.
978-1-7281-7556-0
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/291876
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact