AII aims to infer the most likely future intent based on current aircraft motion states, therefore, it has become an essential method to enhance air traffic situational awareness [1]. Generally, aircraft motion states consist of aircraft IDs, latitude/longitude/altitude coordinates, ground speeds, accelerations and heading angles, which could be directly gained from the surveillance infrastructures like Radars and Automatic Dependent Surveillance-Broadcast (ADS-B) systems. Given current aircraft motion states, one important issue in gaining future air traffic situation prediction is to infer aircraft intent. This is significant because AII plays a fundamental role in conflict detection and avoidance, which hence determines the operational safety of air transportation system.


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    Titel :

    Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area


    Beteiligte:
    Yang, Yang (Autor:in) / Zhang, Jun (Autor:in) / Cao, Xian-bin (Autor:in) / Cai, Kai-quan (Autor:in)


    Erscheinungsdatum :

    01.10.2012


    Format / Umfang :

    1223279 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area

    Yang, Yang / Zhang, Jun / Cao, Xian-bin et al. | IEEE | 2012

    Freier Zugriff

    Terminal-Area Aircraft Intent Inference Approach Based on Online Trajectory Clustering

    Yang Yang / Jun Zhang / Kai-quan Cai | DOAJ | 2015

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    Predicting Aircraft Intent in the Terminal Area

    Bateman, Alec / Lichter, Matthew | AIAA | 2012


    Predicting Aircraft Intent in the Terminal Area

    Bateman, A. / Lichter, M. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2012