Realistic aircraft trajectory models are useful in the design and validation of air traffic management (ATM) systems. Models of aircraft operated under instrument flight rules (IFR) require capturing the variability inherent in how aircraft follow standard flight procedures. The variability in aircraft behavior varies among flight stages. In this paper, we propose a probabilistic model that can learn the variability from the procedural data and flight tracks collected from radar surveillance data. For each segment, a Gaussian mixture model is used to learn the deviations of aircraft trajectories from their procedures. Given new procedures, we can generate synthetic trajectories by sampling a series of deviations from the trained Gaussian distributions and reconstructing the aircraft trajectory using the deviations and the procedures. We extend this method to capture pairwise correlations between aircraft and show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft. We demonstrate the proposed models on the arrival tracks and procedures of the John F. Kennedy International Airport. The distributional similarity between the original and the synthetic trajectory dataset was evaluated using the Jensen-Shannon divergence between the empirical distributions of different variables. We also provide qualitative analyses of the synthetic trajectories generated from the models.
翻译:实际飞机轨迹模型在设计和验证空中交通管理系统方面非常有用。下令飞行规则(IFR)下操作飞机的模型需要捕捉飞机遵循标准飞行程序时固有的变异性。飞机行为的变异性在飞行阶段之间变化。在本文中,我们提出了一个概率模型,该模型可以从雷达监测数据收集的程序数据和飞行轨迹中学习变异性。对于每个段,高斯混合模型用于学习飞机轨迹与其程序的偏差。给定新的程序,我们可以通过从训练的高斯分布中采样一系列偏差,然后使用偏差和程序重新构建飞机轨迹来生成合成轨迹。我们将此方法扩展到捕获飞机之间的成对相关性,并展示了如何使用成对模型生成涉及任意数量飞机的交通流。我们在约翰·肯尼迪国际机场的到达轨迹和程序上演示了所提出的模型。使用不同变量的经验分布之间的Jensen-Shannon散度评估了原始和合成轨迹数据集之间的分布相似性。我们还提供了所生成模型的合成轨迹的定性分析。