Title: Learning Generative Models for Climbing Aircraft from Radar Data Show Chinese title

Title: 从雷达数据中学习攀爬飞机的生成模型

Abstract: Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 26.7% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost. Show Chinese abstract

Abstract: 准确的爬升飞机轨迹预测（TP）受到关于飞机操作的认知不确定性的影响，这可能导致预测轨迹和观测轨迹之间出现显著的误配。 本文提出了一种爬升飞机的生成模型，其中标准的飞机数据基础（BADA）模型通过从数据中学习到的推力功能修正进行了增强。 该方法具有三个特点：与BADA相比，到达时间的预测误差减少了26.7%；生成的轨迹与测试数据相比更加真实；并且能够以最低的计算成本计算置信区间。