Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review is a work by Angelo Coluccia, Gianluca Parisi, and Alessio Fascista.

Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems
are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency-modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper reviews the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification,
and classification.

Fecha de publicación: July 2020

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review contains the following major sections:

  • Introducción
  • Basic Theory for Radar Signal Processing
  • Literature on Drone Detection
  • Literature on Drone Verification and Classification
  • Conclusiones

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Autores: Angelo Coluccia, Gianluca Parisi, and Alessio Fascista

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