Real-Time Track Prediction of CNN-LSTM Model Based on Attention Mechanism
WANG Kun
ZHOU Zhichong
QU Kai
CAO Mingsong
HU Yanda
Abstract：Aimed at the problems that traditional trajectory prediction methods based on mathematical or statistical models have a certain of inherent limitations and are difficult to meet increasingly the demands of efficiency,accuracy,and real-time trajectory prediction in the modern aviation field,a novel real-time traj-ectory prediction method is proposed based on a CNN-LSTM model with an attention mechanism.The proposed model is that multidimensional features are extracted from trajectory data by one-dimensional convolution,reducing the number of input features.Taking the resulting multidimensional time-series da-ta as an input of LSTM,the contextual information can be extracted by LSTM.Moreover,an attention mechanism is employed to assign weights to output from different time-series nodes within the LSTM,fo-cusing on key trajectory information.The experimental validation shows that the proposed model in com-parison with the LSTM model and the CNN-LSTM model,produces trajectory predictions to be even more close to match real trajectories.Specifically,the model in this paper achieves a 29.7％reduction in average prediction error compared to the LSTM model and a 25.4％reduction compared to the CNN-LSTM mod-el.In summary,the proposed method significantly enhances the accuracy of trajectory prediction.
Keywords：flight trajectory predictionattention mechanismconvolutional neural networkrecurrent neural network
Publication Date：2023-12-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 50-57 )