| DOI | Resolve DOI: https://doi.org/10.1109/DASC58513.2023.10311246 |
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| Author | Search for: Mehta, Varun1; Search for: Azad, Hamid; Search for: Dadboud, Fardad; Search for: Bolic, Miodrag; Search for: Mantegh, Iraj1 |
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| Affiliation | - National Research Council Canada. Aerospace
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| Funder | Search for: Defence Research and Development Canada; Search for: National Research Council Canada |
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| Format | Text, Article |
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| Conference | 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), October 1-5, 2023, Barcelona, Spain |
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| Subject | UAV; radar; PTZ; payload; detection; classification; soft sensors; radar detection; sensor fusion; autonomous aerial vehicles; cameras; robustness |
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| Abstract | 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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| Publication date | 2023-10-01 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 5fa93cbd-1e77-4935-8b26-8050667c70fc |
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| Record created | 2024-10-28 |
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| Record modified | 2025-11-03 |
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