SOO-CHANG PEIS. G. Huang2018-09-102018-09-102012-081053587Xhttp://scholars.lib.ntu.edu.tw/handle/123456789/373923https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863916708&doi=10.1109%2fTSP.2012.2197204&partnerID=40&md5=5a6886e228c54333509ee208f77d7a48An 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. © 2012 IEEE.Adaptive time-frequency analysis; chirp rate estimation; concentration measure; instantaneous frequency estimation; ridge detection; time-frequency reassignmentAdaptive time-frequency analysis; Chirp rate estimation; Concentration measures; Instantaneous frequency estimation; Time-frequency reassignments; Principal component analysis; Frequency estimationjournal article10.1109/TSP.2012.21972042-s2.0-84863916708WOS:000306517300012