Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters
This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3948 |
| _version_ | 1797603473512988672 |
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| author | Wei Luo Yongxiang Zhao Quanqin Shao Xiaoliang Li Dongliang Wang Tongzuo Zhang Fei Liu Longfang Duan Yuejun He Yancang Wang Guoqing Zhang Xinghui Wang Zhongde Yu |
| author_facet | Wei Luo Yongxiang Zhao Quanqin Shao Xiaoliang Li Dongliang Wang Tongzuo Zhang Fei Liu Longfang Duan Yuejun He Yancang Wang Guoqing Zhang Xinghui Wang Zhongde Yu |
| author_sort | Wei Luo |
| collection | DOAJ |
| description | This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (<i>f</i>, <i>Q</i>, and <i>R</i>) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation. |
| first_indexed | 2024-03-11T04:32:38Z |
| format | Article |
| id | doaj.art-0520ac4711d7435291f117d95f044ca4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:38Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0520ac4711d7435291f117d95f044ca42023-11-17T21:16:54ZengMDPI AGSensors1424-82202023-04-01238394810.3390/s23083948Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman FiltersWei Luo0Yongxiang Zhao1Quanqin Shao2Xiaoliang Li3Dongliang Wang4Tongzuo Zhang5Fei Liu6Longfang Duan7Yuejun He8Yancang Wang9Guoqing Zhang10Xinghui Wang11Zhongde Yu12North China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaUniversity of Chinese Academy of Sciences, Beijing 101407, ChinaIntelligent Garden and Ecohealth Laboratory (iGE), College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaThis paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (<i>f</i>, <i>Q</i>, and <i>R</i>) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation.https://www.mdpi.com/1424-8220/23/8/3948Procapra przewalskii protectionautonomous unmanned aerial vehicleobject trackingKalman filterlong and short-term memory |
| spellingShingle | Wei Luo Yongxiang Zhao Quanqin Shao Xiaoliang Li Dongliang Wang Tongzuo Zhang Fei Liu Longfang Duan Yuejun He Yancang Wang Guoqing Zhang Xinghui Wang Zhongde Yu Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters Sensors Procapra przewalskii protection autonomous unmanned aerial vehicle object tracking Kalman filter long and short-term memory |
| title | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
| title_full | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
| title_fullStr | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
| title_full_unstemmed | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
| title_short | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
| title_sort | procapra przewalskii tracking autonomous unmanned aerial vehicle based on improved long and short term memory kalman filters |
| topic | Procapra przewalskii protection autonomous unmanned aerial vehicle object tracking Kalman filter long and short-term memory |
| url | https://www.mdpi.com/1424-8220/23/8/3948 |
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