STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/1/248 |
| _version_ | 1827384959287951360 |
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| author | Namkyung Yoon Dongjae Lee Kiseok Kim Taehoon Yoo Hyeontae Joo Hwangnam Kim |
| author_facet | Namkyung Yoon Dongjae Lee Kiseok Kim Taehoon Yoo Hyeontae Joo Hwangnam Kim |
| author_sort | Namkyung Yoon |
| collection | DOAJ |
| description | Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy. |
| first_indexed | 2024-03-08T15:11:51Z |
| format | Article |
| id | doaj.art-ca653311307f491b99ad209c8262b14d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:51Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ca653311307f491b99ad209c8262b14d2024-01-10T14:51:29ZengMDPI AGApplied Sciences2076-34172023-12-0114124810.3390/app14010248STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory DetectionNamkyung Yoon0Dongjae Lee1Kiseok Kim2Taehoon Yoo3Hyeontae Joo4Hwangnam Kim5School of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaAccurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.https://www.mdpi.com/2076-3417/14/1/248UAV trajectoryultra-widebandKalman filterimage interpolationattention mechanism |
| spellingShingle | Namkyung Yoon Dongjae Lee Kiseok Kim Taehoon Yoo Hyeontae Joo Hwangnam Kim STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection Applied Sciences UAV trajectory ultra-wideband Kalman filter image interpolation attention mechanism |
| title | STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection |
| title_full | STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection |
| title_fullStr | STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection |
| title_full_unstemmed | STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection |
| title_short | STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection |
| title_sort | steam spatial trajectory enhanced attention mechanism for abnormal uav trajectory detection |
| topic | UAV trajectory ultra-wideband Kalman filter image interpolation attention mechanism |
| url | https://www.mdpi.com/2076-3417/14/1/248 |
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