Enhancing micro-Doppler classification using Superlet based time-frequency distribution

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Mignone, Luca and Ilioudis, Christos and Clemente, Carmine and Ullo, Silvia (2023) Enhancing micro-Doppler classification using Superlet based time-frequency distribution. IEEE Transactions on Aerospace and Electronic Systems, 59 (6). pp. 9831-9838. ISSN 0018-9251 (https://doi.org/10.1109/TAES.2023.3312064)

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Abstract

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.

ORCID iDs

Mignone, Luca, Ilioudis, Christos ORCID logoORCID: https://orcid.org/0000-0002-7164-6461, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X and Ullo, Silvia;
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