The weather conditions affect the approach and departure routings of aircraft. The detection of flight status of aircraft faces two major issues under adverse weather conditions. They are how to select an approximate and effective feature combination from different flight parameters and how to identify the status of an aircraft via a reasonable flight parameter combination. This article presents a solution to the flight status recognition problem of an aircraft. The time-domain and wavelet-domain features are extracted from a complete flight dataset and a typical flight dataset, respectively. ARMA coefficients entropy is also extracted to represent dynamic behavior of flight data. A new deep sparse learning network with an optimized Gaussian process classifier is proposed to detect the aircraft status. Experiments are executed via a practical flight dataset under different weather. The feasibility of the selection scheme of flight parameters and the proposed deep learning method to detect abnormal flight situation are verified by competitive experimental results, which presents widespread conclusions on feature selection methods and abnormality flight status detection.

中文翻译：

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不同天气条件下的飞行状态识别

天气状况会影响飞机的进场和离场航线。在恶劣的天气条件下，飞机飞行状态的检测面临两个主要问题。它们是如何从不同的飞行参数中选择一个近似有效的特征组合，以及如何通过合理的飞行参数组合来识别飞机的状态。本文提出了一种解决飞机飞行状态识别问题的方法。时域和小波域特征分别从完整的飞行数据集和典型的飞行数据集中提取。还提取了 ARMA 系数熵来表示飞行数据的动态行为。提出了一种具有优化高斯过程分类器的新型深度稀疏学习网络来检测飞机状态。实验是通过不同天气下的实际飞行数据集执行的。通过竞争性实验结果验证了飞行参数选择方案和所提出的深度学习方法检测异常飞行情况的可行性，为特征选择方法和异常飞行状态检测提供了广泛的结论。