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

文献类型： 外文期刊

第一作者： Luo, Wei

作者： Luo, Wei;Zhao, Yongxiang;Li, Xiaoliang;Duan, Longfang;He, Yuejun;Wang, Yancang;Zhang, Guoqing;Wang, Xinghui;Yu, Zhongde;Luo, Wei;Shao, Quanqin;Wang, Dongliang;Luo, Wei;Duan, Longfang;He, Yuejun;Wang, Yancang;Wang, Xinghui;Luo, Wei;Duan, Longfang;He, Yuejun;Wang, Yancang;Wang, Xinghui;Luo, Wei;Shao, Quanqin;Zhang, Tongzuo;Zhang, Tongzuo;Liu, Fei

作者机构：

关键词： Procapra przewalskii protection; autonomous unmanned aerial vehicle; object tracking; Kalman filter; long and short-term memory

期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2023 年 23 卷 8 期

页码：

收录情况： SCI

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.

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