Civil aircraft flight stage identification method based on airborne data

Abstract

The invention belongs to the field of aircraft trajectory analysis application, and particularly relates to a civil aircraft flight stage identification method based on airborne data, which comprisesthe following steps: preprocessing a flight path; dividing a vertical motion situation according to the preprocessed flight path; correcting the divided vertical motion situation; dividing flight stages according to the vertical motion situation; constructing a flight state characteristic model according to the characteristics of the flight stage; and dividing the flight stages according to the preprocessed flight path and the corrected vertical motion situation. Re-identification and re-division of the flight stages are achieved, aircraft models do not need to be distinguished in the implementation process, the execution steps are relatively simple and easy to implement, the problem of flight stage division errors in QAR data can be solved, Technical support is provided for flight stage feature analysis of civil aircrafts.

Description

technical field [0001] The invention belongs to the application field of aircraft trajectory analysis, and in particular relates to a method for identifying flight stages of civil aircraft based on airborne data. Background technique [0002] The division of various flight stages experienced by civil aircraft from take-off to landing in the operation process, in the investigation of safety incidents, performance analysis and flight status monitoring, calculation of fuel consumption and pollutant emissions, evaluation of flight operation efficiency, flight data statistics and prediction, airspace operations It is a very important basic condition in the research field of trajectory analysis such as quality evaluation. For example, in the process of estimating flight fuel consumption based on aircraft type performance, only by accurately identifying the flight phase and aerodynamic configuration of the aircraft can the correct calculation of the aircraft's Sports model and fuel...

AI Technical Summary

Problems solved by technology

[0004] (1) The advantage of tracking the altitude change rate is that it is simple and easy to implement, but it can only be divided into climb, cruise, and descent. Other flight stages need more track information to cooperate. At the same time, this method is locally linear when dealing with altitude jitter. Neither regression nor local mean-smoothing methods are effective in identifying height-varying trends over slightly larger breadths;

[0005] (2) Machine learning has the advantage of high recognition accuracy, but on the one hand, it requires a large amount of actual flight airborne data for training and the implementation process is relatively complicated; on the other hand, it lacks flexibility for new changes outside the training set;

But at the same time, because each field needs to set thresholds according to the characteristics of aircraft performance and corresponding operating rules, the more these fields, the more thresholds are required, and the restrictions on aircraft types and operating rules are stricter, so the flexibility is relatively small. Difference

In addition, the combination of various conditions needs to be considered in the process of calculating the membership degree, and it is impossible to accurately judge the rare special combination conditions, and there is no more effective processing method at present.

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