终端空域中具有高时间分辨率的数据驱动概率轨迹学习
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace
AI总结：
本文提出一种数据驱动的学习框架，结合混合模型与seq2seq神经网络解决误差传播和降维问题，在终端空域数据集上显著提升了长步飞行轨迹预测精度与时间分辨率。
摘要：
预测飞行轨迹是一个极具价值的研究领域。本文提出了一种数据驱动的学习框架，利用混合模型和基于seq2seq的神经网络的预测与特征提取能力，同时解决由误差传播和降维带来的普遍挑战。使用该框架训练后，在给定过去轨迹和上下文信息的情况下，学习到的模型能显著提高长步预测精度。通过将预测轨迹与真实值进行比较，评估了该方法的准确性和有效性。结果表明，所提方法在终端空域飞行轨迹数据集上优于最先进的预测方法。该方法生成的轨迹具有更高的时间分辨率（每秒1个时间步长对比每秒0.1个时间步长），且更接近真实值。
英文摘要：
Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks while addressing prevalent challenges caused by error propagation and dimensionality reduction. After training with this framework, the learned model can improve long-step prediction accuracy significantly given the past trajectories and the context information. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth. The results indicate that the proposed method has outperformed the state-of-the-art predicting methods on a terminal airspace flight trajectory dataset. The trajectories generated by the proposed method have a higher temporal resolution(1 timestep per second vs 0.1 timestep per second) and are closer to the ground truth.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
