  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.
