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Journal ArticleDOI

The fractional Fourier transform and time-frequency representations

Luís B. Almeida
- 01 Nov 1994 - 
- Vol. 42, Iss: 11, pp 3084-3091
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TLDR
The authors briefly introduce the functional Fourier transform and a number of its properties and present some new results: the interpretation as a rotation in the time-frequency plane, and the FRFT's relationships with time- frequencies such as the Wigner distribution, the ambiguity function, the short-time Fouriertransform and the spectrogram.
Abstract
The functional Fourier transform (FRFT), which is a generalization of the classical Fourier transform, was introduced a number of years ago in the mathematics literature but appears to have remained largely unknown to the signal processing community, to which it may, however, be potentially useful. The FRFT depends on a parameter /spl alpha/ and can be interpreted as a rotation by an angle /spl alpha/ in the time-frequency plane. An FRFT with /spl alpha/=/spl pi//2 corresponds to the classical Fourier transform, and an FRFT with /spl alpha/=0 corresponds to the identity operator. On the other hand, the angles of successively performed FRFTs simply add up, as do the angles of successive rotations. The FRFT of a signal can also be interpreted as a decomposition of the signal in terms of chirps. The authors briefly introduce the FRFT and a number of its properties and then present some new results: the interpretation as a rotation in the time-frequency plane, and the FRFT's relationships with time-frequency representations such as the Wigner distribution, the ambiguity function, the short-time Fourier transform and the spectrogram. These relationships have a very simple and natural form and support the FRFT's interpretation as a rotation operator. Examples of FRFTs of some simple signals are given. An example of the application of the FRFT is also given. >

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Citations
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Signal reconstruction from two close fractional Fourier power spectra

TL;DR: In this paper, the angular derivative of the fractional Fourier transform power spectrum is derived from the knowledge of two close fractional power spectra, which can be used for phase retrieval.
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Designing high-resolution time-frequency and time-scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance

TL;DR: This paper deals with the problem of extracting information from non-stationary signals in the form of features that can be used for effective decision-making in both data analysis and machine learning for automatic classification systems, and forms a TF/TS feature set including two complementary categories: signal related features and image features.
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SAR imaging of moving targets using polynomial Fourier transform

TL;DR: An efficient algorithm is proposed that can be used for radar images containing both moving and stationary targets and can be extended to higher-order and order-adaptive polynomial FT forms.
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Effect of fractional Fourier transformation on time-frequency distributions belonging to the Cohen class

TL;DR: The Cohen (1989) class of time-frequency distributions, which can be obtained from the Wigner distribution by convolving it with a kernel characterizing that distribution, is considered, confirming the important role this transform plays in the study of such representations.
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Eigenvalues and eigenvectors of generalized DFT, generalized DHT, DCT-IV and DST-IV matrices

TL;DR: The eigenvalues and eigenvectors of the generalized discrete Fourier transform, the GDHT, Hartley transform, DCT-IV, and DST-IV matrices, and the type-IV discrete sine transform matrices are investigated in a unified framework to demonstrate the effectiveness of fractional transforms.
References
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Journal ArticleDOI

Time-frequency distributions-a review

TL;DR: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented with emphasis on the diversity of concepts and motivations that have gone into the formation of the field.
Journal ArticleDOI

Linear and quadratic time-frequency signal representations

TL;DR: A tutorial review of both linear and quadratic representations is given, and examples of the application of these representations to typical problems encountered in time-varying signal processing are provided.
Journal ArticleDOI

The Fractional Order Fourier Transform and its Application to Quantum Mechanics

TL;DR: In this article, a generalized operational calculus is developed, paralleling the familiar one for the ordinary transform, which provides a convenient technique for solving certain classes of ordinary and partial differential equations which arise in quantum mechanics from classical quadratic hamiltonians.
Journal ArticleDOI

Image rotation, Wigner rotation, and the fractional Fourier transform

TL;DR: In this article, the degree p = 1 is assigned to the ordinary Fourier transform and the degree P = 1/2 to the fractional transform, where p is the degree of the optical fiber.
Journal ArticleDOI

Time-frequency representation of digital signals and systems based on short-time Fourier analysis

TL;DR: In this article, the authors developed a representation for discrete-time signals and systems based on short-time Fourier analysis and showed that a class of linear-filtering problems can be represented as the product of the time-varying frequency response of the filter multiplied by the short time Fourier transform of the input signal.
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