Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters

	Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters
	Luo, Wei; Zhao, Yongxiang; Shao, Quanqin; Li, Xiaoliang; Wang, Dongliang; Zhang, Tongzuo; Liu, Fei; Duan, Longfang; He, Yuejun; Wang, Yancang
刊名	SENSORS
	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.
内容类型	期刊论文
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).