Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters
文献类型：期刊论文
| 作者 | Luo, Wei1,2,3,4; Zhao, Yongxiang; Shao, Quanqin1,5; Li, Xiaoliang; Wang, Dongliang1; Zhang, Tongzuo5,6; Liu, Fei7; Duan, Longfang2,3; He, Yuejun2,3; Wang, Yancang2,3 |
| 刊名 | SENSORS
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 23期号:8页码:3948 |
| 关键词 | Procapra przewalskii protection autonomous unmanned aerial vehicle object tracking Kalman filter long and short-term memory |
| DOI | 10.3390/s23083948 |
| 文献子类 | Article |
| 英文摘要 | 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 (f, Q, and R) 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. |
| 学科主题 | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS关键词 | MASK R-CNN |
| 语种 | 英语 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193495]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.North China Inst Aerosp Engn, Langfang 065000, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Aerosp Remote Sensing Informat Proc & Applicat Col, Langfang 065000, Peoples R China 4.Natl Joint Engn Res Ctr Space Remote Sensing Infor, Langfang 065000, Peoples R China 5.Chinese Acad Agr Sci, Agr Informat Inst, Key Lab Agr Monitoring & Early Warning Technol, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China 6.Univ Chinese Acad Sci, Beijing 101407, Peoples R China 7.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810001, Peoples R China 8.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Intelligent Garden & Ecohlth Lab iGE, 866 Yuhangtang Rd, Hangzhou 310058, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Wei,Zhao, Yongxiang,Shao, Quanqin,et al. Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters[J]. SENSORS,2023,23(8):3948. |
| APA | Luo, Wei.,Zhao, Yongxiang.,Shao, Quanqin.,Li, Xiaoliang.,Wang, Dongliang.,...&Yu, Zhongde.(2023).Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters.SENSORS,23(8),3948. |
| MLA | Luo, Wei,et al."Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters".SENSORS 23.8(2023):3948. |
入库方式： OAI收割
来源：地理科学与资源研究所
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