TY - GEN
T1 - A Time-varying Filtering Algorithm based on Short-time Fractional Fourier Transform
AU - Wu, Longwen
AU - Zhao, Yaqin
AU - He, Liang
AU - He, Shengyang
AU - Ren, Guanghui
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - This paper presents a novel time-varying filtering (TVF) algorithm based on order time-varying short-time fractional Fourier transform (OTV-STFrFT) for multi-component signal analysis, which can process non-linear frequency modulated (NLFM). The idea of combining TVF and the order time-varying STFrFT are mainly inspired by the following two aspects: i) NLFM signals can be locally regarded as segmented linear frequency modulated (LFM) signals; ii) the fractional Fourier transform is the optimal sparse representation of LFM signal. The order time-varying STFrFT can overcome several defects of the existing TVF algorithms in dealing with multicomponent signals, of which the mixed components may intersect in the time-frequency distribution. The numerical results shows that the proposed algorithm is superior to the TVF algorithms based on conventional short-time Fourier transform (STFT) and state-of-the-art synchrosqueezed wavelet transforms (SsWT) in multi-component signal analysis.
AB - This paper presents a novel time-varying filtering (TVF) algorithm based on order time-varying short-time fractional Fourier transform (OTV-STFrFT) for multi-component signal analysis, which can process non-linear frequency modulated (NLFM). The idea of combining TVF and the order time-varying STFrFT are mainly inspired by the following two aspects: i) NLFM signals can be locally regarded as segmented linear frequency modulated (LFM) signals; ii) the fractional Fourier transform is the optimal sparse representation of LFM signal. The order time-varying STFrFT can overcome several defects of the existing TVF algorithms in dealing with multicomponent signals, of which the mixed components may intersect in the time-frequency distribution. The numerical results shows that the proposed algorithm is superior to the TVF algorithms based on conventional short-time Fourier transform (STFT) and state-of-the-art synchrosqueezed wavelet transforms (SsWT) in multi-component signal analysis.
KW - Multi-component signal decomposition.
KW - Order time-varying
KW - Short-time fractional Fourier transform
KW - Time-varying filtering
UR - https://www.scopus.com/pages/publications/85083431362
U2 - 10.1109/ICNC47757.2020.9049737
DO - 10.1109/ICNC47757.2020.9049737
M3 - 会议稿件
AN - SCOPUS:85083431362
T3 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
SP - 555
EP - 560
BT - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
Y2 - 17 February 2020 through 20 February 2020
ER -