Synchrosqueezing Superlet Transform: Algorithm and Applications

Time frequency analysis (TFA) plays a critical role in processing nonstationary signals, but conventionalmethods suffer from limited time-frequency representation (TFR) clarity and energy diffusion, particularly for highly nonstationary signals. In this article, we propose the synchrosqueezing superlet transform (SSLT), which improves TF concentration by combining the super-resolution framework of the superlet transform (SLT) with the energy reassignment capability of the synchrosqueezing transform (SST). By addressing the resolution limitations imposed by the Heisenberg–Gabor uncertainty in SST preprocessing, SSLT achieves superior clarity and energy concentration, particularly for rapidly varying and highly nonstationary signals. Furthermore, it introduces an improved signal reconstruction mechanism, enhancing both robustness and accuracy. Validated on simulated and realworld engineering signals, SSLT demonstrates superior time frequency (TF) concentration, sharper TFRs, and precise IF estimation. These results confirm the efficacy and practicality of SSLT in advanced TFA and fault diagnostics.