可解释航班着陆参数学习的隧道高斯过程模型
A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters
摘要：
进近和着陆事故在世界范围内造成了大量的船体损失。技术（例如，仪表着陆系统）和程序（例如，稳定的方法标准），以减少风险。在本文中，我们提出了一种数据驱动的方法来学习和解释飞行的方法和着陆参数，以促进对飞行动力学的可理解和可操作的见解。具体而言，我们开发了两种变体的隧道高斯过程（TGP）模型，以阐明飞机的方法和着陆动力学使用先进的表面运动制导和控制系统（A-SMGCS）的数据，然后表明飞行的稳定性。TGP混合了稀疏变分高斯过程和极高斯过程的优点，可以在柱坐标系下从大量数据中学习。我们研究TGP定性和定量合成三个复杂的轨迹数据集，并比较TGP对现有的轨迹学习方法。经验上，三峡工程表现出优越的建模性能。当应用于操作A-SMGCS数据时，TGP提供着陆动力学的生成概率描述和可解释的进近和着陆参数的隧道视图。这些概率通道模型可以促进程序遵守的分析，并在进近和着陆程序期间增强现有机组人员和空中交通管制员的显示，从而实现必要的纠正措施。
英文摘要：
Approach and landing accidents have resulted in a significant number of hull losses worldwide. Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce the risks. In this paper, we propose a data-driven method to learn and interpret flight's approach and landing parameters to facilitate comprehensible and actionable insights into flight dynamics. Specifically, we develop two variants of tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. We examine TGP qualitatively and quantitatively by synthesizing three complex trajectory datasets and compared TGP against existing methods on trajectory learning. Empirically, TGP demonstrates superior modeling performance. When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable tunnel views of approach and landing parameters. These probabilistic tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controllers' displays during the approach and landing procedures, enabling necessary corrective actions.
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