In complex substation environments, the flight safety problem when unmanned aerial vehicles perform inspection tasks becomes more and more prominent. Currently, the flight control system is the main means of UAV flight status monitoring, but it lacks in-depth analysis of flight data, especially flight trajectory data. To make full use of the flight control system data, this paper proposes an anomaly detection and trajectory prediction method. Firstly, the local temporal features of the original data in the subspace are extracted, and the anomaly detection of the flight data is achieved by measuring the changes of the data subspace vectors on the basis of reducing the computational complexity of the data, followed by dynamically adjusting the thresholds of the anomalous data by using one-class support vector machines. And finally, the UAV trajectory prediction is performed by using a time-series based LSTM neural network, focusing on the anomalies that may affect the safety of flights Data Early Warning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on UAV Data Anomaly Detection and Early Trajectory Warning Technology


    Contributors:


    Publication date :

    2025-01-17


    Size :

    877268 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Trajectory anomaly detection system and online trajectory anomaly detection method

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

    Free access

    Traffic anomaly detection early warning method and system based on artificial intelligence

    YAN JUN / FENG SHU / WANG WEI | European Patent Office | 2024

    Free access

    Expressway confluence area vehicle conflict early warning method based on trajectory data

    CHEN LU / AN KANG | European Patent Office | 2024

    Free access

    TRAJECTORY DEVIATION EARLY WARNING METHOD, TERMINAL, AND STORAGE MEDIUM

    LI JIE / CHEN YIJUN / CAO LAN et al. | European Patent Office | 2024

    Free access

    Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics

    Belhadi, Asma / Djenouri, Youcef / Srivastava, Gautam et al. | IEEE | 2022