With the rapid growth of air traffic, it often happens that aircraft deviate from the original flight plan during actual flight. This paper proposes an anomaly detection method for aircraft trajectory deviation to realize single-point and successive multipoint anomaly detection from a data-driven perspective. Given the one-to-many relationship between reporting points of planned and real trajectories, a matching algorithm is used to match these points. Four trajectory deviation features (which are the position deviation, distance deviation, altitude deviation, and flight stage) are defined. On this basis, a one-class support vector machine is trained to detect single-point anomalies using the deviation features as input. Furthermore, successive multipoint anomaly detection of the aircraft is realized by considering the deviation of successive segments of the trajectory. Taking the flights taking off and landing at four Chinese hub airports as examples, the proposed method obtained an F score, which is a balance of the precision and recall, over 0.92, indicating it achieves high accuracy for anomaly detection.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data-Driven Method for Detecting Flight Trajectory Deviation Anomaly


    Contributors:
    Guo, Ziyi (author) / Yin, Chang (author) / Zeng, Weili (author) / Tan, Xianghua (author) / Bao, Jie (author)

    Published in:

    Publication date :

    2022-12-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    METHOD AND DEVICE TO ESTIMATE COSTS OF DEVIATION IN A FLIGHT TRAJECTORY

    MERE JEAN-CLAUDE | European Patent Office | 2015

    Free access


    Fast local representation of gravity anomaly along flight trajectory

    Zhou, Huan / An, Xueying / Zheng, Wei et al. | SAGE Publications | 2016


    A Data-Driven Methodology for Pre-Flight Trajectory Prediction

    Zazzaro, Gaetano / Martone, Francesco / Romano, Gianpaolo et al. | TIBKAT | 2022

    Free access

    Trajectory anomaly detection system and online trajectory anomaly detection method

    LI WENBIN / YAO DI / BI JINGPING | European Patent Office | 2024

    Free access