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

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.