A novel solution for improved performance of Time-frequency concentration

被引:7
|
作者
Guo, Juan [1 ,3 ]
Hao, Guocheng [1 ,2 ,3 ]
Yu, Jiantao [1 ]
Wang, Panpan [1 ]
Jin, Yarui [1 ]
机构
[1] China Univ Geosci, Sch Mech Engn & Elect Informat, Wuhan 430074, Hubei, Peoples R China
[2] Duke Univ, Dept Math, Durham, NC 27708 USA
[3] Hubei Key Lab Adv Control & Intelligent Automat Co, Wuhan 430074, Peoples R China
关键词
Non-stationary signals; short-time fractional Fourier transform; Synchroextracting transform; Time-frequency concentration; Variational mode decomposition; FRACTIONAL FOURIER-TRANSFORM; REPRESENTATION; SIGNALS;
D O I
10.1016/j.ymssp.2022.109784
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
To improve the time-frequency (TF) concentration performance of the short-time fractional Fourier transform (STFrFT), and solve the multi-order matching problem of multi-component signals, this paper introduces a new algorithm that is referred to as the Variational mode decomposition-short-time fractional Fourier transform-Synchroextracting transform (VSSTFrFT). This work employs the Variational mode decomposition (VMD) algorithm to decompose multi -component signals into single-component sets, then the STFrFT algorithm matches the optimal rotation order of each component separately to solve the multi-order matching problem. Finally, this paper utilizes the Synchroextracting transform (SET) to extract the TF coefficient of ridgeline position in STFrFT distribution, improving the concentration performance. For different types of signals, the VSSTFrFT algorithm can get a higher TF concentration than the traditional TFA methods. In the application of engineering measured data, the VSSTFrFT algorithm can extract the modalities of the signals and display the frequency curve clearly, which can be used for structural complexity and time-varying characteristics analysis in practical engineering applications.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] From Time-Frequency to Vertex-Frequency and Back
    Stankovic, Ljubisa
    Lerga, Jonatan
    Mandic, Danilo
    Brajovic, Milos
    Richard, Cedric
    Dakovic, Milos
    MATHEMATICS, 2021, 9 (12)
  • [42] Kernel Estimation for Time-Frequency Distribution
    Deprem, Zeynel
    Cetin, A. Enis
    2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 1973 - 1976
  • [43] Time-frequency analysis application to the evaluation of instantaneous combustion noise
    D'Ambrosio, Stefano
    Ferrari, Alessandro
    Jin, Zhiru
    FUEL, 2022, 312
  • [44] A nonlinear time-frequency analysis method
    Karimi-Ghartemani, M
    Ziarani, AK
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2004, 52 (06) : 1585 - 1595
  • [45] Time-frequency methods for coherent spectroscopy
    Volpato, Andrea
    Collini, Elisabetta
    OPTICS EXPRESS, 2015, 23 (15): : 20040 - 20050
  • [46] ADAPTIVE GABOR FRAMES BY PROJECTION ONTO TIME-FREQUENCY SUBSPACES
    Doerfler, Monika
    Angelo Velasco, Gino
    2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [47] Parameterized Resampling Time-Frequency Transform
    Li, Tianqi
    He, Qingbo
    Peng, Zhike
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2022, 70 : 5791 - 5805
  • [48] Time-Frequency Analysis as Probabilistic Inference
    Turner, Richard E.
    Sahani, Maneesh
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (23) : 6171 - 6183
  • [49] Reduced Interference Sparse Time-Frequency Distributions for Compressed Observations
    Jokanovic, Branka
    Amin, Moeness
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (24) : 6698 - 6709
  • [50] Small Target Classification of Maritime Radars Using Standardized Time-Frequency Distribution Images and Residual Networks With Improved Focal Loss
    Li, Jing-Yi
    Zhang, Xiao-Jun
    Zhao, Zi-Jian
    Shui, Peng-Lang
    Xu, Shu-Wen
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2025, 74