|Table of Contents|

A flight phase identification method based on airborne data of civil aircraft(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年01期
Page:
216-228
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
A flight phase identification method based on airborne data of civil aircraft
Author(s):
WANG Bing1; 2;  ZHANG Ying1; 2;  XIE Hua1; 2;  LI Jie1; 2
(1. College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106,Jiangsu, China; 2. National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China)
Keywords:
civil aircraft;  airborne QAR trajectories;  flight phase identification;  DBSCAN method;  identification of vertical movement;  flight state characteristic
PACS:
V247
DOI:
10.19818/j.cnki.1671-1637.2022.01.018
Abstract:
To resolve the flight phase identification errors in the airborne quick access recorder(QAR)trajectory data of civil aircraft, a method of flight phase re-identification was proposed based on the aerodynamic configuration and vertical movements of aircraft. This method comprises four steps: preprocessing of QAR data, identification of vertical movements, construction of a flight state characteristics model, and re-identification of flight phases. A DBSCAN-based local traversal clustering method was used to cluster the trends of pressure altitude to classify vertical movements, and a valid minimum state persistence time was set to eliminate the local flutter in the pressure altitude data. Considering aircraft-to-aircraft differences in flap position and control, fluctuations in airfield QFE, and inaccuracies in the airspeed indicator during low-speed taxiing, a flight-state characteristics model suitable for all types of aircraft and based on state parameters, such as flap switch position, landing gear position, ground speed, and vertical movements, was constructed. The model was used to divide the QAR data into flight state characteristic segments. The relationship model between each flight phase and flight state characteristics was established, and all flight state feature segments were identified as corresponding flight stages combined with landing gear air-ground logic. Three typical sample flights are used as examples, calculation results show that all the flight phases(including go-arounds)are correctly identified and divided, and are also fully consistent with the flap and landing gear states of the aircraft. The flight phase identification error in raw QAR data fields is solved effectively. The flight phases of QAR tracks of 272 268 flights are re-identified, and the success rate is 99.7%. The average durations of non-clean configuration flight phases, such as take-off, initial climb, approach, and landing, are 0.6, 1.9, 6.1 and 4.0 min, respectively, and the average altitudes from the ground are 54, 3 680, 6 030 and 2 500 ft, respectively, which are consistent with the actual flight operation behaviors. Therefore, the flight-phase re-identification method can be applied to numerous flights and provide technical support in analyzing the characteristics of civil aircraft flight phases. 5 tabs, 8 figs, 25 refs.

References:

[1] IMMER M, JURETZKO P G. Advanced aircraft performance analysis[J]. Aircraft Engineering and Aerospace Technology, 2018, 90(4): 627-638.
[2] SUN J Z, ELLERBROEK J, HOEKSTRA J M. WRAP: an open-source kinematic aircraft performance model[J]. Transportation Research Part C: Emerging Technologies, 2019, 98: 118-138.
[3] SUN J Z, HOEKSTRA J M, ELLERBROEK J. Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 391-404.
[4] POLL D I A, SCHUMANN U. An estimation method for the fuel burn and other performance characteristics of civil transport aircraft during cruise. Part 2: determining the aircraft's characteristic parameters[J]. The Aeronautical Journal, 2021, 125(1284): 296-340.
[5] POLL D I A, SCHUMANN U. An estimation method for the fuel burn and other performance characteristics of civil transport aircraft in the cruise. Part 1: fundamental quantities and governing relations for a general atmosphere[J]. The Aeronautical Journal, 2021, 125(1284): 257-295.
[6] WANG Bing, LI Jie, LI Chi, et al. A method for computing flight operation fuel burn and emissions based on ADS-B trajectories[J]. Journal of Aeronautics, Astronautics and Aviation, 2020, 52(2): 183-196.
[7] XUE Da-bin, LIU Zhi-zhao, WANG Bing, et al. Impacts of COVID-19 on aircraft usage and fuel consumption: a case study on four Chinese international airports[J]. Journal of Air Transport Management, 2021, 95: 102106.
[8] SUN Jun-zi, DEDOUSSI I. Evaluation of aviation emissions and environmental costs in Europe using OpenSky and OpenAP[J]. Engineering Proceedings, 2021, 13(1): 1-9.
[9] RAJEE O. Safety analysis of automatic dependent surveillance-broadcast(ADS-B)system[J]. International Journal of Aerospace and Mechanical Engineering, 2018, 5(2): 9-18.
[10] TABASSUM A, SEMKE W. UAT ADS-B data anomalies and the effect of flight parameters on dropout occurrences[J]. Data, 2018, 3(2): 1-21.
[11] ALI B S, TAIB N A. A study on geometric and barometric altitude data in automatic dependent surveillance broadcast(ADS-B)Messages[J]. Journal of Navigation, 2019, 72: 1140-1158.
[12] SEMKE W, ALLEN N, TABASSUM A, et al. Analysis of radar and ADS-B influences on aircraft detect and avoid(DAA)systems[J]. Aerospace, 2017, 4(3): 1-14.
[13] WANG Lei, WU Chang-xu, SUN Rui-shan. An analysis of flight quick access recorder(QAR)data and its applications in preventing landing incidents[J]. Reliability Engineering and System Safety, 2014, 127: 86-96.
[14] WANG Lei, ZHANG Jing-yi, DONG Chuan-ting, et al. A method of applying flight data to evaluate landing operation performance[J]. Ergonomics, 2019, 62(2): 171-180.
[15] ZHANG Ming, HUANG Qian-wen, LIU Si-han, et al. Fuel consumption model of the climbing phase of departure aircraft based on flight data analysis[J]. Sustainability, 2019, 11(16): 4362.
[16] CHARLES J, ADRIAN R, ANTHONY B, et al. Phase of flight and rule of flight calculator[C]∥ICRAT. 7th International Conference on Research in Air Transportation. Philadelphia: ICRAT, 2016: 1-8.
[17] GOBLET V, FALA N, MARAIS K. Identifying phases of flight in general aviation operations[C]∥AIAA. 15th AIAA Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2015: 1-20.
[18] FU T C. A review on time series data mining[J]. Engineering Applications of Artificial Intelligence, 2011, 24(1): 164-181.
[19] MIKE P, ROBERT O. Determination of horizontal and vertical phase of flight in recorded air traffic data[C]∥AIAA. AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston: AIAA, 2006: 1-16.
[20] 张 余,葛 飞.基于航迹数据的飞行状态识别方法研究[J].航空计算技术,2017,47(6):45-48,51.ZHANG Yu, GE Fei. Research on flight state recognition method based on track data[J]. Aeronautical Computing Technique, 2017, 47(6): 45-48, 51.(in Chinese)
[21] NGUYEN T H, WARD D, NGUYEN T, et al. A neural-network based inference engine for a general aviation pilot advisor[C]∥AIAA. 35th Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 1997: 1-10.
[22] TIAN Feng, CHENG Xiao-ke, MENG Guang-lei, et al. Research on flight phase division based on decision tree classifier[C]∥IEEE. 2017 2nd IEEE International Conference on Computational Intelligence and Applications. New York: IEEE, 2017: 372-375.
[23] HARRAL V Q. Genetic algorithms for tuning fuzzy membership functions in flight control software[D]. Texas: Texas A&M University, 1995.
[24] ZADEH L. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
[25] KELLY W E, PAINTER J H. Flight segment identification as a basis for pilot advisory systems[J]. Journal of Aircraft, 2006, 43(6): 1628-1635.
[26] SUN Jun-zi, ELLERBROEK J, HOEKSTRA J. Large-scale flight phase identification from ADS-B data using machine learning methods[C]∥TU Delft. 7th International Conference on Research in Air Transportation. Delft: TU Delft, 2016: 1-7.
[27] SUN Jun-zi, ELLERBROEK J, HOEKSTRA J. Flight extraction and phase identification for large automatic dependent surveillance-broadcast datasets[J]. Journal of Aerospace Information Systems, 2017, 14(10): 566-571.
[28] ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]∥AIAA. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Reston: AIAA, 1996: 226-231.
[29] 王 兵.ADS-B历史飞行轨迹数据清洗方法[J].交通运输工程学报,2020,20(4):217-226.WANG Bing. Data cleaning method of ADS-B historical flight trajectories[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 217-226.(in Chinese)
[30] SAXENA A, PRASAD M, GUPTA A, et al. A review of clustering techniques and developments[J]. Neurocomputing, 2017, 267: 664-681.

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Last Update: 2022-03-20