基于时空特征融合的Encoder-Decoder多步4D短期航迹预测  

Multi-step 4D Short-term Trajectory Prediction Using Encoder-Decoder with Spatio-Temporal Features Fusion

作　　者：石庆研[1] 张泽中 韩萍[1] SHI Qingyan;ZHANG Zezhong;HAN Ping(Tianjin Key Lab for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室,天津300300

出　　处：《信号处理》2023年第11期2037-2048,共12页Journal of Signal Processing

基　　金：民航局安全能力建设资金项目(KJZ49420210082)。

摘　　要：航迹预测在确保空中交通安全、高效运行中扮演着至关重要的角色。所预测的航迹信息是航迹优化、冲突告警等决策工具的输入,而预测准确性取决于模型对航迹序列特征的提取能力。航迹序列数据是具有丰富时空特征的多维时间序列,其中每个变量都呈现出长短期的时间变化模式,并且这些变量之间还存在着相互依赖的空间信息。为了充分提取这种时空特征,本文提出了基于融合时空特征的编码器-解码器(Spatio-Temporal EncoderDecoder,STED)航迹预测模型。在Encoder中使用门控循环单元(Gated Recurrent Unit,GRU)、卷积神经网络(Convolutional Neural Network,CNN)和注意力机制(Attention,AT)构成的双通道网络来分别提取航迹时空特征,Decoder对时空特征进行拼接融合,并利用GRU对融合特征进行学习和递归输出,实现对未来多步航迹信息的预测。利用真实的航迹数据对算法性能进行验证,实验结果表明,所提STED网络模型能够在未来10 min预测范围内进行高精度的短期航迹预测,相比于LSTM、CNN-LSTM和AT-LSTM等数据驱动航迹预测模型具有更高的精度。此外,STED网络模型预测一个航迹点平均耗时为0.002 s,具有良好的实时性。The trajectory prediction plays a crucial role in ensuring the safety and efficient operation of air traffic.The predicted trajectory information serves as input for decision-making tools such as trajectory optimization and conflict alerts,and the accuracy of prediction depends on the model’s ability to extract features from the trajectory sequence.The trajectory sequence data is a multidimensional time series with rich spatio-temporal characteristics,where each variable exhibits long-term and short-term temporal patterns,and there are also spatial dependencies among these variables.To fully capture these spatio-temporal features,this paper proposes an Encoder-Decoder model based on the fusion of spatio-temporal features,referred to as the Spatio-Temporal Encoder-Decoder(STED)model.In the Encoder,a dual-channel network consisting of gated recurrent unit(GRU),convolutional neural network(CNN),and attention mechanisms(AT)is employed to separately extract the spatio-temporal features of the trajectory.The Decoder concatenates and fuses the spatiotemporal features,and utilizes GRU to learn and recursively output the fused features,thereby achieving multi-step prediction of future trajectory information.The performance of the algorithm is validated using real trajectory data.Experimental results demonstrate that the proposed STED network model achieves high accuracy in short-term trajectory prediction within a prediction range of 10 minutes.It outperforms data-driven trajectory prediction models such as LSTM,CNNLSTM,and AT-LSTM in terms of accuracy.Furthermore,the STED network model predicts a trajectory point with an average time consumption of 0.002 seconds,demonstrating excellent real-time performance.

关 键 词：4D航迹预测 时空特征 Encoder-Decoder 门控循环单元 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]