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
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