河南科技大学学报（自然科学版）2025，Vol.46Issue(1)：25-33,9.DOI:10.15926/j.cnki.issn1672-6871.2025.01.004
基于自注意力机制与高斯混合变分自编码器的飞行轨迹聚类方法研究
Study on Flight Trajectory Clustering Method Based on Self-Attention Mechanism and Gaussian Mixture Variational Autoencoder
摘要
Abstract
In order to accurately identify flight trajectory patterns,a flight trajectory clustering method based on Self-Attention mechanism(SA)and Gaussian Mixture Variational Autoencoder(GMVAE)is proposed.SA-GMVAE is an end-to-end deep clustering method.GMVAE uses variational inference to estimate the potential distribution of each trajectory,maps the input flight trajectory data to the potential space composed of multiple Gaussian distributions,and performs clustering according to the trajectory distribution characteristics.Considering that GMVAE cannot take into account the global key information of potential features,the Self-Attention mechanism is embedded in the encoder to capture global dependencies and automatically assign weights during feature extraction,so as to highlight key features and improve trajectory clustering effect.Finally,the approach flight trajectory data set of Tianjin Binhai International Airport is taken as an example to verify the effectiveness of the model.The experimental results show that:Compared with K-means,DBSCAN,DTW+HDBSCAN,AE+DP and AE+GMM,the contour coefficients of SA-GMVAE are increased by 27.6%,20.2%,18.2%,18.6%and 15.7%,respectively.Compared with the GMVAE clustering model without Self-Attention mechanism,the profile coefficient is increased by 9.5%,which can cluster the flight trajectory more accurately.关键词
飞行轨迹/模式识别/变分自编码器/自注意力机制Key words
flight trajectory/pattern recognition/variational autoencoder/self-attention mechanism引用本文复制引用
张召悦,李莎,鲍水达..基于自注意力机制与高斯混合变分自编码器的飞行轨迹聚类方法研究[J].河南科技大学学报（自然科学版）,2025,46(1):25-33,9.基金项目
国家重点研发计划项目(KJZ25420200012) （KJZ25420200012）
中央高校基本科研项目(3122022105) （3122022105）
