Classical time-frequency (TF) distributions, as the short time Fourier transform (STFT) or the continuous wavelet transform (CWT), aim to enhance either the resolution in time or frequency, or attempt to strike a balance between the two. In this article, we demonstrate how a super resolution technique, the superlet-based TF distribution, named superlet transform (SLT), can boost the performance of existing classification algorithms relying on information extraction from the micro-Doppler signature. SLT is applied to provide a TF distribution with finer resolutions that would boost the performance of micro-Doppler classification approaches based on TF distributions (TFDs). This work shows the effectiveness of the integration of SLT in the processing pipeline with verification on real radar data.


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    Titel :

    Enhancing Micro-Doppler Classification Using Superlet-Based Time-Frequency Distribution


    Beteiligte:
    Mignone, Luca (Autor:in) / Ilioudis, Christos (Autor:in) / Clemente, Carmine (Autor:in) / Ullo, Silvia (Autor:in)


    Erscheinungsdatum :

    2023-12-01


    Format / Umfang :

    2576939 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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