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

Abstract

Classical time-frequency 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 paper, we demonstrate how a super resolution technique, the Superlet based time frequency 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 time-frequency distribution with finer resolutions that would boost the performance of micro-Doppler classification approaches based on time-frequency distributions (TFDs). This work shows the effectiveness of the integration of SLT in the processing pipeline with verification on real radar data.