Anomaly detection for a noncooperative satellite is a fundamental and significant step in space situation awareness. To effectively detect the abnormal state of an on-orbit satellite, this article proposes a novel generative adversarial network with a time–frequency (TF) spectrum (TF-GAN). First, the TF spectrum of radar echoes is acquired, and the artificially selected features are extracted from the TF spectrum of the normal state of the on-orbit satellite. Second, the proposed model is trained to learn the TF spectrum latent representation and the pre-extracted features. Then, the trained model can distinguish abnormal samples from the normal ones and output the evaluated anomaly score. Finally, the proposed TF-GAN is compared with several GAN-based methods. Experiments show that the accuracy and F1-score of the TF-GAN are superior to those of the comparison methods, and the anomaly scores of abnormal and normal classes are more separable.


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    Titel :

    TF-GAN: Satellite Anomaly Detection via Generative Adversarial Networks and Time–Frequency Spectrum


    Beteiligte:
    Jiao, Jian (Autor:in) / Li, Gang (Autor:in) / Wang, Jianwen (Autor:in) / Zhao, Zhichun (Autor:in) / Li, Jun (Autor:in) / Chen, Hongmeng (Autor:in)


    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    18801383 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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