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Reconstruction of Aircraft States During Landing Based on Quick Access Recorder Data

2017; American Institute of Aeronautics and Astronautics; Volume: 40; Issue: 9 Linguagem: Inglês

10.2514/1.g002637

1533-3884

Lukas Höhndorf, Joachim Siegel, Javensius Sembiring, Phillip Koppitz, Florian Holzapfel,

Human-Automation Interaction and Safety

No AccessThe Kalman Filter and Its Aerospace ApplicationsReconstruction of Aircraft States During Landing Based on Quick Access Recorder DataLukas Höhndorf, Joachim Siegel, Javensius Sembiring, Phillip Koppitz and Florian HolzapfelLukas HöhndorfTechnical University of Munich, 85748 Garching bei München, Germany, Joachim SiegelTechnical University of Munich, 85748 Garching bei München, Germany, Javensius SembiringTechnical University of Munich, 85748 Garching bei München, Germany, Phillip KoppitzTechnical University of Munich, 85748 Garching bei München, Germany and Florian HolzapfelTechnical University of Munich, 85748 Garching bei München, GermanyPublished Online:12 Apr 2017https://doi.org/10.2514/1.G002637SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Safety Management Manual (SMM), 3rd ed., International Civil Aviation Organization, Doc. 9859, Montreal, 2013, pp. 2-18–2-23. Google Scholar[2] Statistical Summary of Commercial Jet Airplane Accidents–Worldwide Operations 1959–2015, Aviation Safety, Boeing Commercial Airplanes, Seattle, WA, 2016. Google Scholar[3] Höhndorf L., Siegel J., Sembiring J., Koppitz P. and Holzapfel F., "Reconstruction of Aircraft Trajectories During Landing Using a Rauch-Tung-Striebel Smoother, Instrument Landing System Deviation Information, and Taxiway Locations," AIAA Aviation Conference, Atmospheric Flight Mechanics, AIAA Paper 2016-3705, 2016. doi:https://doi.org/10.2514/6.2016-3705 LinkGoogle Scholar[4] "Flight Data Recorder Read-Out Technical and Regulatory Aspects," Bureau d'Enquêtes et d'Analyses pour la Sécurité de l'Aviation Civile Study, Le Bourget, France, 2005. Google Scholar[5] Jategaonkar R. V., Flight Vehicle System Identification, AIAA, Reston, VA, 2007, pp. 76–94. Google Scholar[6] Kálmán R. E., "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, Vol. 82, No. 1, 1960, pp. 35–45. doi:https://doi.org/10.1115/1.3662552 JBAEAI 0021-9223 CrossrefGoogle Scholar[7] Rauch H. E., Striebel C. T. and Tung F., "Maximum Likelihood Estimates of Linear Dynamic Systems," AIAA Journal, Vol. 3, No. 8, 1965, pp. 1445–1450. doi:https://doi.org/10.2514/3.3166 AIAJAH 0001-1452 LinkGoogle Scholar[8] Zipfel P. H., Modeling and Simulation of Aerospace Vehicle Dynamics, 3rd ed., AIAA, Reston, VA, 2014, pp. 83, 91. LinkGoogle Scholar[9] Annex 10 to the Convention on International Civil Aviation, Aeronautical Telecommunications, Vol. 1, Radio Navigation Aids, 6th ed., International Civil Aviation Organization, Montreal, 2006. Google Scholar[10] MATLAB Optimization Toolbox, User's Guide, Ver. R2015b, MathWorks, Natick, MA, pp. 6-1–6-10. Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byRobust Data-Driven Fault Detection: An Application to Aircraft Air Data SensorsInternational Journal of Aerospace Engineering, Vol. 2022Anomaly detection based on multivariate data for the aircraft hydraulic system17 September 2020 | Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Vol. 235, No. 5Predictive maintenance framework of the aircraft system based on PHM informationModeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method18 August 2020 | Sensors, Vol. 20, No. 16Research on Anomaly Detection of Civil Aircraft Hydraulic System Based on Multivariate Monitoring DataModeling of the Aircraft's Low Energy State During the Final Approach Phase Using Operational Flight DataXiaolong Wang, Javensius Sembiring, Phillip Koppitz, Lukas Höhndorf, Chong Wang and Florian Holzapfel6 January 2019 What's Popular Volume 40, Number 9September 2017Special Issue on The Kalman Filter and Its Aerospace Applications CrossmarkInformationCopyright © 2017 by the Institute of Flight System Dynamics at Technical University of Munich. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the ISSN 0731-5090 (print) or 1533-3884 (online) to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerodromesAeronauticsAircraft Components and StructureAircraft DesignAircraft InstrumentsAircraft OperationsAircraft Operations and TechnologyAircraft Stability and ControlAircraftsAirport InfrastructureAirportsAviationCivil AircraftFlight Control SurfacesFlight RecorderForced LandingTakeoff and Landing KeywordsModel AircraftQuick Access RecorderTaxiwayRauch Tung Striebel (RTS) SmootherAircraft TrajectoryInstrument Landing SystemFlight Data MonitoringAccelerometerWorld Geodetic SystemData AnalysisAcknowledgmentsThe majority of work presented in this Note is based on the student's thesis "Touchdown Point Reconstruction Based on Operational Quick Access Recorder Data" of Joachim Siegel. This thesis was supervised by Lukas Höhndorf and Javensius Sembiring and was submitted at the Institute of Flight System Dynamics on November 18th, 2015. This Note summarizes the developed techniques of that thesis for the reconstruction of the trajectory of a landing aircraft. A further aspect that was investigated in the mentioned thesis but not in this Note is the application of flight dynamic models to determine the touchdown point.PDF Received29 November 2016Accepted22 February 2017Published online12 April 2017