Flight Situation Recognition under Different Weather Conditions

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.