| DOI | Trouver le DOI : https://doi.org/10.1109/DASC58513.2023.10311246 |
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| Auteur | Rechercher : Mehta, Varun1; Rechercher : Azad, Hamid; Rechercher : Dadboud, Fardad; Rechercher : Bolic, Miodrag; Rechercher : Mantegh, Iraj1 |
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| Affiliation | - Conseil national de recherches Canada. Aérospatiale
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| Bailleur de fonds | Rechercher : Defence Research and Development Canada; Rechercher : National Research Council Canada |
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| Format | Texte, Article |
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| Conférence | 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), October 1-5, 2023, Barcelona, Spain |
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| Sujet | UAV; radar; PTZ; payload; detection; classification; soft sensors; radar detection; sensor fusion; autonomous aerial vehicles; cameras; robustness |
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| Résumé | Unmanned Aerial Vehicles (UAVs) have experienced remarkable progress and widespread utilization, highlighting the need for robust detection and classification systems to ensure safety and security. This paper presents a comprehensive study on UAV and payload detection and classification, emphasizing the fusion of multiple data sources to enhance accuracy and reliability. A fusion system integrating radar and Pan-Tilt-Zoom (PTZ) camera data is developed and evaluated. Experimental results demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of > 94% for UAV detection and > 90% for payload classification. The findings underscore the system's potential for real-world applications in UAV and payload detection and classification scenarios, addressing the growing demands in this field. |
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| Date de publication | 2023-10-01 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 5fa93cbd-1e77-4935-8b26-8050667c70fc |
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| Enregistrement créé | 2024-10-28 |
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| Enregistrement modifié | 2025-11-03 |
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