基于注意力机制的CNN-LSTM模型的航迹预测

基于数学模型或统计模型的传统航迹预测方法存在一定的局限性，无法满足现代航空领域对于高效、准确、实时的航迹预测需求。针对此问题，提出基于注意力机制的CNN-LSTM模型的实时航迹预测方法。该模型首先使用一维卷积对航迹数据的多维度特征进行提取，从而减少输入特征的数量。其次利用获取的多维度时序数据作为LSTM的输入，通过LSTM提取上下文的信息。最后使用注意力机制为LSTM中不同时序节点的输出赋予权重，达到聚焦关键航迹信息的作用。经过实验验证：本文的模型与LSTM模型和CNN-LSTM模型相比，预测出的路径更接近真实航迹；文中的模型比LSTM模型的平均预测误差降低了29.7%，比CNN-LSTM模型降低了25.4%。综上所述，文中方法可以显著提高航迹预测的精度。

Real-Time Track Prediction of CNN-LSTM Model Based on Attention Mechanism

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 trajectory 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 data 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, focusing on key trajectory information. The experimental validation shows that the proposed model in comparison 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 model. In summary, the proposed method significantly enhances the accuracy of trajectory prediction.