This paper presents a machine learning algorithm to detect ionospheric scintillation and classify scintillation events based on training data in the frequency domain. The detector input is the signal intensity. Validation using data from Ascension Island, Hong Kong, and Peru shows 91-96% accuracy of scintillation detection. Different combinations of training data, observation matrices, and learning algorithms are investigated to obtain performance measures. Testing results on data from Singapore demonstrate the general capabilities of the detector.


    Access

    Download


    Export, share and cite



    Title :

    Automatic Equatorial GPS Amplitude Scintillation Detection Using a Machine Learning Algorithm


    Contributors:
    Yu Jiao (author) / Hall, John J. (author) / Morton, Yu T. (author)


    Publication date :

    2017-02-01


    Size :

    2089235 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    GPS Amplitude Fading Due to Ionospheric Scintillation Near the Equatorial Ionospheric Anomaly

    Gladek, Yuri C. / Sousasantos, Jonas / Salles, Lucas et al. | AIAA | 2019


    Nowcasting of Amplitude Ionospheric Scintillation Based on Machine Learning Techniques

    Carvalho, Otavio / da Silva de Almeida Nava Alves, Pedro Augusto Araujo / de La Cruz Cueva, Ricardo Yvan et al. | IEEE | 2022



    Performance evaluation of an automatic gps ionospheric phase scintillation detector using a machine‐learning algorithm

    Jiao, Yu / Hall, John J. / Morton, Yu T. | British Library Online Contents | 2017