• DocumentCode
    52512
  • Title

    General Parameterized Time-Frequency Transform

  • Author

    Yang, Yi ; Peng, Z.K. ; Dong, X.J. ; Zhang, W.M. ; Meng, Guang

  • Author_Institution
    State Key Lab. of Mech. Syst. & Vibration, Shanghai Jiao Tong Univ., Shanghai, China
  • Volume
    62
  • Issue
    11
  • fYear
    2014
  • fDate
    1-Jun-14
  • Firstpage
    2751
  • Lastpage
    2764
  • Abstract
    Interest in parameterized time-frequency analysis for non-stationary signal processing is increasing steadily. An important advantage of such analysis is to provide highly concentrated time-frequency representation with signal-dependent resolution. In this paper, a general scheme, named as general parameterized time-frequency transform (GPTF transform), is proposed for carrying out parameterized time-frequency analysis. The GPTF transform is defined by applying generalized kernel based rotation operator and shift operator. It provides the availability of a single generalized time-frequency transform for applications on signals of different natures. Furthermore, by replacing kernel function, it facilitates the implementation of various parameterized time - frequency transforms from the same standpoint. The desirable properties and the dual definition in the frequency domain of GPTF transform are also described in this paper. One of the advantages of the GPTF transform is that the generalized kernel can be customized to characterize the time - frequency signature of non-stationary signal. As different kernel formulation has bias toward the signal to be analyzed, a proper kernel is vital to the GPTF. Thus, several potential kernels are provided and discussed in this paper to develop the desired parameterized time - frequency transforms. In real applications, it is desired to identify proper kernel with respect to the considered signal. This motivates us to propose an effective method to identify the kernel for the GPTF.
  • Keywords
    signal processing; time-frequency analysis; transforms; GPTF transform; concentrated time-frequency representation; general parameterized time-frequency transform; generalized kernel based rotation operator; kernel formulation; nonstationary signal processing; parameterized time-frequency analysis; shift operator; signal-dependent resolution; Approximation methods; Chirp; Kernel; Polynomials; Signal resolution; Time-frequency analysis; Transforms; General parameterized time-frequency transform; kernel formulation; time frequency analysis; time-frequency concentration;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/TSP.2014.2314061
  • Filename
    6778757