2012Unpublished venueRequires access

Fractional Fourier transform

B. T. Krishna

Open publisher page 13 citations

Abstract

The Fractional Fourier transform (FRFT), which provides generalization of conventional Fourier Transform was introduced many years ago in mathematics literature by Namias. In this paper, definition, properties of fractional Fourier transform and its relationship with other transforms is discussed. Various definitions of discrete version of FRFT and their comparison is presented. FRFT falls under the category of Linear time frequency representations. Some of the applications of FRFT such as detection of signals in noise, image compression, reduction of side lobe levels using convolutional windows, and time-frequency analysis are illustrated with examples. It has been observed that FRFT can be used in more effective manner compared to Fourier transform with additional degrees of freedom.

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What this paper is about

The Fractional Fourier transform (FRFT), which provides generalization of conventional Fourier Transform was introduced many years ago in mathematics literature by Namias. In this paper, definition, properties of fractional Fourier transform and its relationship with other transforms is discussed. Various definitions of discrete version of FRFT and their comparison is presented. FRFT falls under the category of Linear time frequency representations. Some of the applications of FRFT such as detection of signals in noise, image compression, reduction of side lobe levels using convolutional windows, and time-frequency analysis are illustrated with examples. It has been observed that FRFT can be used in more effective manner compared to Fourier transform with additional degrees of freedom.

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Available abstract

The Fractional Fourier transform (FRFT), which provides generalization of conventional Fourier Transform was introduced many years ago in mathematics literature by Namias. In this paper, definition, properties of fractional Fourier transform and its relationship with other transforms is discussed. Various definitions of discrete version of FRFT and their comparison is presented. FRFT falls under the category of Linear time frequency representations. Some of the applications of FRFT such as detection of signals in noise, image compression, reduction of side lobe levels using convolutional windows, and time-frequency analysis are illustrated with examples. It has been observed that FRFT can be used in more effective manner compared to Fourier transform with additional degrees of freedom.

Key concepts: Computer science, Fractional Fourier transform, Fourier transform, Discrete Fourier transform (general), Fourier analysis, Mathematics, Mathematical analysis

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