空军工程大学学报2023，Vol.24Issue(6)：50-57,8.DOI:10.3969/j.issn.2097-1915.2023.06.007
基于注意力机制的CNN-LSTM模型的航迹预测
Real-Time Track Prediction of CNN-LSTM Model Based on Attention Mechanism
王堃 1周志崇 2曲凯 2曹明松 2胡延达3
作者信息
- 1. 93886部队,乌鲁木齐,830001
- 2. 空军工程大学空管领航学院,西安,710051
- 3. 陕西师范大学计算机科学学院,西安,710119
- 折叠
摘要
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.关键词
航迹预测/注意力机制/卷积神经网络/循环神经网络Key words
flight trajectory prediction/attention mechanism/convolutional neural network/recurrent neural network分类
信息技术与安全科学引用本文复制引用
王堃,周志崇,曲凯,曹明松,胡延达..基于注意力机制的CNN-LSTM模型的航迹预测[J].空军工程大学学报,2023,24(6):50-57,8.
