中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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, Wei; Zhao, Yongxiang; Shao, Quanqin; Li, Xiaoliang; Wang, Dongliang; Zhang, Tongzuo; Liu, Fei; Duan, Longfang; He, Yuejun; Wang, Yancang
刊名SENSORS
出版日期2023
卷号23期号:8
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.
源URL[http://210.75.249.4/handle/363003/61665]  
专题西北高原生物研究所_中国科学院西北高原生物研究所
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).
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).
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).

入库方式: OAI收割

来源:西北高原生物研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。