Multiple squeezes from adaptive chirplet transform

Abstract

Time-frequency (TF) analysis method is an effective tool to analyze non-stationary signals. However, how to generate a clear TF representation for strongly time-varying signals is still a challenging task. In this paper, we propose a new TF analysis method, named multi-synchrosqueezing chirplet transform (MSSCT), to study the non-stationary features of strongly time-varying signals. By relaxing the hypothesis made on the amplitude and frequency modulations, the proposed method produces a more accurate instantaneous frequency (IF) estimator to correct the deviation caused by TF ridge. MSSCT is an upgraded version of the recently developed multi-synchrosqueezing transform (MSST) method, and can be viewed as a good combination of adaptive chirplet transform and synchrosqueezing technique. Moreover, we prove that MSSCT retains the signal reconstruction ability. Simulated and real-life signals are employed to validate the advantages of MSSCT by comparing with some advanced TF methods. The experimental results demonstrate that MSSCT can provide a better time-varying description, obtaining a more precise IF estimate, being stable for the selection of window length, and having a stronger noise robustness.