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

Autor: Wei Luo, Yongxiang Zhao, Quanqin Shao, Xiaoliang Li, Dongliang Wang, Tongzuo Zhang, Fei Liu, Longfang Duan, Yuejun He, Yancang Wang, Guoqing Zhang, Xinghui Wang, Zhongde Yu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Sensors, Vol 23, Iss 8, p 3948 (2023)
Druh dokumentu: article
ISSN: 23083948
1424-8220
DOI: 10.3390/s23083948
Popis: 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.
Databáze: Directory of Open Access Journals
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