Communication Dans Un Congrès Année : 2023

Abnormal Behavior State-of-the-Art for UAV Detection in Complex Environments

État de l’art pour la détection de comportements anormaux d’UAV dans des environnements complexes

Résumé

The global drone market has surged, growing from $1.6 billion in 2015 to $5.6 billion in 2020. Despite their increasing prevalence, drones can pose challenges. For instance, drone sightings at Gatwick Airport in December 2018 disrupted around 1,000 flights, highlighting potential misuses and the need for effective regulation. Given such incidents, the effective detection, tracking, and identification of abnormal behaviors of small UAVs in complex environments become critical for ensuring security and mitigating potential threats. This paper presents a state-of-the-art review of abnormal behavior detection of small UAVs, focusing on the role of data fusion in enhancing detection performance, especially when dealing with heterogeneous data from multiple sensors. Our research offers a structured overview of abnormal behavior detection methods and emphasizes the role of data fusion in addressing challenges, especially in environments with multiple operating drones like Amazon’s delivery system. In addition, our research highlights the need to promote standardization of performances measures used to abnormal behavior detection algorithms. While metrics like precision, MOTA, and accuracy are standard for detection, tracking, and classification respectively, evaluating behavior detection in a data fusion system remains a challenge. This paper provides a comprehensive review of current methods for detecting abnormal behaviors of small UAVs and the role of data fusion in enhancing detection performance. The findings and conclusions presented in this paper can contribute to a deeper understanding of the challenges and opportunities in employing data fusion techniques for counter-UAV applications and help guide future research efforts in this domain. The paper also suggests ways to enhance collaboration within NATO and promote the development of standardized performance measures to facilitate comparison and improvement of detection techniques.

Fichier principal
Vignette du fichier
MP-SET-315-10.pdf (625.33 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04513213 , version 1 (20-03-2024)

Licence

Identifiants

Citer

Pierre PATHE, Benjamin Pannetier, Bartheye Olivier, Daniel Lieutenant-Colonel Gigan. Abnormal Behavior State-of-the-Art for UAV Detection in Complex Environments. Detection, Tracking, ID and Defeat of Small UAVs in Complex Environments -- STO-MP-SET-315, NATO - OTAN -- Science & Technology Organization, Oct 2023, Copenhagen, Denmark. ⟨10.14339/STO-MP-SET-315⟩. ⟨hal-04513213⟩
224 Consultations
381 Téléchargements

Altmetric

Partager

  • More