摘要
为解决机场场面滑行冲突、等待时间长等运行问题,保证场面安全的同时提高服务水平,增加机场吞吐量,针对机器学习模型性能依赖于良好数据集的现状,提出一种基于注意力机制、融合门控循环单元(GRU)和改进卡尔曼滤波算法(IKF)的场面航空器滑行轨迹预测模型。使用3个独立的门控循环单元网络来捕获航空器未来时刻的运动状态和时间上的依赖性,并引入注意力机制加强提取数据差异性特征的能力,学习输入到输出的映射关系;与改进后的扩展卡尔曼滤波器融合,将神经网络输出的结果整合到状态预测和更新过程,以提高预测轨迹序列的准确性。利用禄口机场航空器真实滑行轨迹对所提模型的有效性进行验证,仿真结果表明:所提模型能够对场面航空器滑行轨迹进行有效准确的预测,总体均方误差约为0.00128,相较于单一循环神经网络(RNN)、长短时记忆(LSTM)网络及GRU模型,均方根误差(RMSE)分别减小72.9%,54.7%和39.9%,预测耗时40 ms,可以准确、快速预测滑行轨迹,为降低机场场面管理系统运行负荷提供帮助。
The prediction of aircraft taxiing trajectory helps to solve operational problems such as taxiing conflicts and long waiting times at airports,ensuring surface safety while improving service levels and increasing airport throughput.A model is proposed to predict the taxiing trajectory of a surface aircraft based on an attention mechanism that combines gated recurrent units(GRU)and an improved Kalman filter algorithm(IKF).This addresses the current situation where the performance of machine learning models depends on good data sets.In order to better extract data discrepancy features and learn input-to-output mapping relationships,three independent networks of gated recurrent units are first used to capture the future moment motion states and temporal dependencies of the aircraft.An enhanced extended Kalman filter is then fused with the neural network outputs to integrate them into the state prediction and update process,ultimately improving the predicted trajectory sequence accuracy.Finally,the validity of the model was verified using real aircraft taxi trajectories at Lukou Airport.The simulation results show that the proposed model can effectively and accurately predict aircraft taxi trajectories at the field with an overall mean square error of approximately 0.00128.Compared with the single recurrent neural network(RNN),long and short-term memory network(LSTM)and GRU model,the root mean square error(RMSE)is reduced by 72.9%,54.7%and 39.9%respectively,and the prediction time is 40 ms,which could accurately and quickly predict the taxiing trajectory and provide assistance in reducing the operating load of the airport surface management system.
作者
刘雨生
汤新民
任宣铭
LIU Yusheng;TANG Xinmin;REN Xuanming(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;College of Transportation Science and Engineering,Civil Aviation University of China,Tianjin 300300,China)
出处
《北京航空航天大学学报》
2025年第3期1028-1036,共9页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家重点研发计划(2021YFB1600500)
国家自然科学基金(61773202,52072174)
中国航空无线电电子研究所航空电子系统综合技术国防科技重点实验室基金(6142505180407)
中国民航管理干部学院民航通用航空运行重点实验室开放基金(CAMICKFJJ-2019-04)。
作者简介
通信作者:汤新民.E-mail:tangxinmin@nuaa.edu.cn。
