The recent widespread implementation of Automatic Dependent Surveillance–Broadcasting (ADS-B) systems on aircraft allows for improved monitoring and air traffic control management. As part of this monitoring, it is important to be able to detect unusual flight trajectories due to weather events, detection avoidance, aircraft malfunction, or other activities that may signal anomalous behavior. Given the large volume of ADS-B data available from aircraft around the world, the ability to automatically determine the shape of the trajectory and identify anomalous behavior is important to reduce the need for human identification and labeling. A neural network model is developed for multicategory classification of the shape of the trajectory using features derived from a large ADS-B data set such as bearing and curvature. The results suggest promise in differentiating common trajectory shapes using key factors, with the accuracy of the classifier being comparable to human accuracy.

最近在飞机上广泛实施的自动相关监视广播 (ADS-B) 系统允许改进监视和空中交通管制管理。作为此监控的一部分，重要的是能够检测由于天气事件、检测规避、飞机故障或其他可能发出异常行为信号的活动而导致的异常飞行轨迹。鉴于来自世界各地飞机的大量 ADS-B 数据，自动确定轨迹形状和识别异常行为的能力对于减少人工识别和标记的需求非常重要。使用从大型 ADS-B 数据集（例如方位角和曲率）导出的特征，开发了一种神经网络模型，用于轨迹形状的多类别分类。