摘要
为有效应对飞行轨迹预测中存在的复杂时空特性以及时域波动对预测精度带来的挑战,提出融合时空双重提取与频域增强的飞行轨迹预测方法.该方法结合时间卷积网络(TCN)与iTransformer模型旨在同时捕捉飞行轨迹序列中的局部时序特征与全局变量交互关系,从而在不同层次和粒度上实现对数据特征的双重提取,有效挖掘其潜在的时空关联性.随后引入频域增强通道注意力机制(FECAM),通过离散余弦变换将轨迹特征转化为频域,并应用通道注意力机制强化转化后的频域信息,以减少时域波动带来的影响.实验基于三维飞行轨迹数据集,在爬升、巡航及降落阶段,该方法的平均绝对误差分别为1.15、0.15和0.82.结果表明相较于现有方法,所提方法在预测精度和稳定性方面均具有明显优势.
To address the complex spatiotemporal characteristics and temporal domain fluctuation challenges in flight trajectory prediction,this study proposes a method integrating spatiotemporal dual extraction and frequency-domain enhancement.The proposed method combines the temporal convolutional network(TCN)with the iTransformer model to capture local temporal features and global variable interactions in trajectory sequences.This enables dual extraction of data features at different levels and granularities,effectively uncovering potential spatiotemporal correlations.The frequency enhanced channel attention mechanism(FECAM)is introduced,which converts trajectory features into the frequency domain using the discrete cosine transform and strengthens the frequency-domain information with channel attention,reducing the impact of temporal domain fluctuations.Experiments on a 3D flight trajectory dataset show that during climb,cruise,and descent phases,the proposed method achieves mean absolute error of 1.15,0.15,and 0.82,respectively,demonstrating significant advantages in prediction accuracy and stability over existing methods.
作者
骆晓宁
王燕妮
谷卓
LUO Xiao-Ning;WANG Yan-Ni;GU Zhuo(College of Information and Control Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China;Shaanxi Lingyun Technology Co.Ltd.,Xi’an 710119,China)
出处
《计算机系统应用》
2026年第1期228-236,共9页
Computer Systems & Applications
基金
陕西省自然科学基础研究计划(2025JC-YBMS-791)。
关键词
飞行轨迹预测
时空双重特征提取模块
频域增强通道注意力机制
iTransformer
时间卷积网络
flight trajectory prediction
spatiotemporal dual feature extraction module(SDFEM)
frequency enhanced channel attention mechanism(FECAM)
iTransformer
temporal convolutional network(TCN)
