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

Luca Mignone, Christos Ilioudis, Carmine Clemente, Silvia Ullo

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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.

Original languageEnglish
Pages (from-to)9831-9838
Number of pages8
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume59
Issue number6
Early online date5 Sept 2023
DOIs
Publication statusPublished - 1 Dec 2023

Keywords

  • time-frequency representation
  • micro-Doppler
  • superlet transform
  • human motion recognition

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