Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters
Author:
Luo Wei12345ORCID, Zhao Yongxiang1, Shao Quanqin26, Li Xiaoliang1, Wang Dongliang2ORCID, Zhang Tongzuo67, Liu Fei8ORCID, Duan Longfang134, He Yuejun134, Wang Yancang134, Zhang Guoqing1, Wang Xinghui134, Yu Zhongde1
Affiliation:
1. North China Institute of Aerospace Engineering, Langfang 065000, China 2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 3. Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China 4. National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China 5. Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Key Laboratory of Agricultural Monitoring and Early Warning Technology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China 6. University of Chinese Academy of Sciences, Beijing 101407, China 7. Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China 8. Intelligent Garden and Ecohealth Laboratory (iGE), College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
Abstract
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.
Funder
National Natural Science Foundation of China Open Fund of Key Laboratory of Agricultural Monitoring and Early Warning Technology, Ministry of Agriculture and Rural Affairs Open Fund of Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs Innovation Fund of Production, Study and Research in Chinese Universities Central Government Guided Local Science and Technology Development Fund Project National Key Research and Development Plan “Establishment of Spectral Earth with Medium Spatial Resolution and Its Application Research” Hebei Province Full-time Introduction of Top Talent Research Project National Science and Technology Major Project “Application and Demonstration of High Resolution Remote Sensing Monitoring Platform for Ecological Environment in Xiong’an New Area” High Resolution Earth Observation System National Science and Technology Major Project National Basic Research Plan Project Doctoral Research Startup Fund Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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