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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22947 |
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| _version_ | 1866911361801388032 |
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| author | Tao, Yi Gao, Zhen Ye, Fangquan Xu, Jingbo Song, Tao Li, Weidong Su, Yu Peng, Lu Wu, Xiaomei Qin, Tong Li, Zhongxiang Zheng, Dezhi |
| author_facet | Tao, Yi Gao, Zhen Ye, Fangquan Xu, Jingbo Song, Tao Li, Weidong Su, Yu Peng, Lu Wu, Xiaomei Qin, Tong Li, Zhongxiang Zheng, Dezhi |
| contents | The development of the low-altitude economy has led to a growing prominence of uncrewed aerial vehicle (UAV) safety management issues. Therefore, accurate identification, real-time localization, and effective countermeasures have become core challenges in airspace security assurance. This paper introduces an integrated UAV management and control system based on deep learning, which integrates multimodal multi-sensor fusion perception, precise positioning, and collaborative countermeasures. By incorporating deep learning methods, the system combines radio frequency (RF) spectral feature analysis, radar detection, electro-optical identification, and other methods at the detection level to achieve the identification and classification of UAVs. At the localization level, the system relies on multi-sensor data fusion and the air-space-ground integrated communication network to conduct real-time tracking and prediction of UAV flight status, providing support for early warning and decision-making. At the countermeasure level, it adopts comprehensive measures that integrate ``soft kill'' and ``hard kill'', including technologies such as electromagnetic signal jamming, navigation spoofing, and physical interception, to form a closed-loop management and control process from early warning to final disposal, which significantly enhances the response efficiency and disposal accuracy of low-altitude UAV management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Multimodal Multi-Sensor Fusion-Based UAV Identification, Localization, and Countermeasures for Safeguarding Low-Altitude Economy Tao, Yi Gao, Zhen Ye, Fangquan Xu, Jingbo Song, Tao Li, Weidong Su, Yu Peng, Lu Wu, Xiaomei Qin, Tong Li, Zhongxiang Zheng, Dezhi Signal Processing The development of the low-altitude economy has led to a growing prominence of uncrewed aerial vehicle (UAV) safety management issues. Therefore, accurate identification, real-time localization, and effective countermeasures have become core challenges in airspace security assurance. This paper introduces an integrated UAV management and control system based on deep learning, which integrates multimodal multi-sensor fusion perception, precise positioning, and collaborative countermeasures. By incorporating deep learning methods, the system combines radio frequency (RF) spectral feature analysis, radar detection, electro-optical identification, and other methods at the detection level to achieve the identification and classification of UAVs. At the localization level, the system relies on multi-sensor data fusion and the air-space-ground integrated communication network to conduct real-time tracking and prediction of UAV flight status, providing support for early warning and decision-making. At the countermeasure level, it adopts comprehensive measures that integrate ``soft kill'' and ``hard kill'', including technologies such as electromagnetic signal jamming, navigation spoofing, and physical interception, to form a closed-loop management and control process from early warning to final disposal, which significantly enhances the response efficiency and disposal accuracy of low-altitude UAV management. |
| title | Intelligent Multimodal Multi-Sensor Fusion-Based UAV Identification, Localization, and Countermeasures for Safeguarding Low-Altitude Economy |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.22947 |