STFT with Adaptive Window Width Based on the Chirp Rate

Abstract

An adaptive time-frequency representation (TFR) with higher energy concentration usually requires higher complexity. Recently, a low-complexity adaptive short-time Fourier transform (ASTFT) based on the chirp rate has been proposed. To enhance the performance, this method is substantially modified in this paper: i) because the wavelet transform used for instantaneous frequency (IF) estimation is not signal-dependent, a low-complexity ASTFT based on a novel concentration measure is addressed; ii) in order to increase robustness to IF estimation error, the principal component analysis (PCA) replaces the difference operator for calculating the chirp rate; and iii) a more robust Gaussian kernel with time-frequency-varying window width is proposed. Simulation results show that our method has higher energy concentration than the other ASTFTs, especially for multicomponent signals and nonlinear FM signals. Also, for IF estimation, our method is superior to many other adaptive TFRs in low signal-to-noise ratio (SNR) environments.

Accepted by IEEE Transactions on Signal Processing

References in corpus (1)

• Time-frequency analysis of chaotic systems

Cited by in corpus (4)

• Differentiable adaptive short-time Fourier transform with respect to the window length
• Linear and synchrosqueezed time-frequency representations revisited. Part II: Resolution, reconstruction and concentration
• Interpretable Super-Resolution via a Learned Time-Series Representation
• High-resolution chirplet transform: from parameters analysis to parameters combination