全球定位系统2024，Vol.49Issue(5)：34-43,10.DOI:10.12265/j.gnss.2024078
基于深度学习的载体滚转角估计方法研究
Research on roll angle estimation method based on deep learning
摘要
Abstract
Attitude measurement technology serves as a fundamental component in monitoring vehicle motion states and ensuring safety.The spinning motion of vehicles leads to a coupling of attitude angles,which significantly impacts flight control.In this paper,a deep learning approach based on the long short-term memory(LSTM)neural network is proposed to determine the real-time roll angle of a vehicle.The energy characteristics exhibited by a single antenna receiving satellite signals during the vehicle's rolling state have been analyzed in detail.A correlation between the real-time roll angle and the energy amplitude of the received signal has been established.The influence of changing satellite positions on these measurements is also discussed.Subsequently,the LSTM neural network training method is employed to extract periodic variation features from the measured signals,thereby obtaining various network parameters.These parameters are then used to predict and denoise the received signal,with the roll angle being computed by model matching.To validate the efficacy of the proposed method,a rolling experiment was conducted.The experimental results demonstrate that the LSTM-based deep learning approach effectively restores the features of the received signals,enabling accurate real-time measurement of the vehicle's roll angle.关键词
GNSS/深度学习/长短期记忆(LSTM)神经网络/滚转角/天线增益Key words
GNSS/deep learning/long short-term memory(LSTM)/roll angle/antenna gain分类
天文与地球科学引用本文复制引用
冯璐,吴鹏,郑昱,张竹娴..基于深度学习的载体滚转角估计方法研究[J].全球定位系统,2024,49(5):34-43,10.基金项目
湖南省普通高等学校科技创新团队支持计划 （）
湖南省科技厅重点研发项目(2022GK2026,2024JK2062) （2022GK2026,2024JK2062）
湖南省自然资源厅科技计划项目(2023-78) （2023-78）
湖南省教育厅科研计划重点项目(23A0609) （23A0609）
