The fractional Fourier transform: theory, implementation and error analysis
TL;DR: It is hoped that this implementation and fixed-point error analysis will lead to a better understanding of the issues involved in finite register length implementation of the discrete fractional Fourier transform and will help the signal processing community make better use of the transform.
About: This article is published in Microprocessors and Microsystems.The article was published on 2003-11-03. It has received 128 citations till now. The article focuses on the topics: Fractional Fourier transform & Non-uniform discrete Fourier transform.
Citations
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TL;DR: An efficient CORDIC-based architecture for computing discrete fractional Fourier transform (DFrFT) is proposed which brings down the computational complexity and hardware requirements and provides the flexibility to change the user defined fractional angles to compute DFrFT on-the-fly.
Abstract: Since decades, the fractional Fourier transform (FrFT) has attracted researchers from various domains such as signal and image processing applications. These applications have been essentially demanding the requirement of low computational complexity of FrFT. In this paper, FrFT is simplified to reduce the complexity, and further an efficient CORDIC-based architecture for computing discrete fractional Fourier transform (DFrFT) is proposed which brings down the computational complexity and hardware requirements and provides the flexibility to change the user defined fractional angles to compute DFrFT on-the-fly. Architectural design and working method of proposed architecture along with its constituent blocks are discussed. The hardware complexity and throughput of the proposed architecture are illustrated as well. Finally, the architecture of DFrFT of the order sixteen is implemented using Verilog HDL and synthesized targeting an FPGA device ”XLV5LX110T”. The hardware simulation is performed for functional verification, which is compared with the MATLAB simulation results. Further, the physical implementation result of the proposed design shows that the design can be operated at a maximum frequency of 154 MHz with the latency of 63-clock cycles.
5 citations
01 Aug 2017
TL;DR: Fractional Fourier Transform is applied to denoise the noisy speech signal with fewer thresholds and SNR value, the MAE is minimum and PSNR is good.
Abstract: The Fractional Fourier Transform (FRFT) technique is derived from classical Fourier Transform. It is used in many applications. In this paper Fractional Fourier Transform is applied to denoise the noisy speech signal. The white Gaussian Noise is considered here which is affecting the speech signal. The performance of Fractional Fourier Transform is evaluated for different SNR and hard thresholding value. The results are presented by the measuring parameters MAE and PSNR. It is observed with fewer thresholds and SNR value, the MAE is minimum and PSNR is good.
5 citations
Cites background or methods from "The fractional Fourier transform: t..."
...The basic implementation of Fractional Fourier Transform and its applications are explained in [1]....
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...FRACTIONAL FOURIER TRANSFORM (FRFT) Fractional Fourier Transform is an extended form of Fourier Transform [1]....
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TL;DR: A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper and outperforms several methods in terms of parameter estimation accuracy and efficiency.
Abstract: A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.
5 citations
TL;DR: In this paper, the authors provide an interpretation of the Talbot effect in the frame of the FrFT and derive simple summation formulas between the Fourier transform of a function and the function itself.
Abstract: The Talbot effect, or self-imaging, is a fascinating feature of Fresnel diffraction, where an input periodic wavefront is periodically recovered after specific propagation distances through free space. Interestingly, the Fresnel propagator shows a great similarity to the fractional Fourier transform (FrFT). In this paper, we provide an interpretation of the Talbot effect in the frame of the FrFT and derive simple summation formulas between the FrFT of a function and the function itself. In particular, we show that both the FrFT and the Fourier transform (FT) of any input function can be generated by coherent addition of spatially shifted replicas of the function itself, multiplied by a quadratic phase term. Transposed into the temporal domain, these results may have important applications for real-time analog computation of the FrFT/FT of arbitrary signals.
5 citations
TL;DR: In this article, a model of parameter estimation is established and the factors which influence estimation accuracy are analyzed using two kinds of common sampling-type DFRFT (discrete fractional Fourier transform) algorithm.
5 citations
References
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TL;DR: 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. >
1,698 citations
"The fractional Fourier transform: t..." refers background in this paper
...It is a well-documented fact that the FRFT of a signal corresponds to the rotation of the Wigner distribution of that signal by the required angle a [1]....
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Book•
31 Jan 2001
TL;DR: The fractional Fourier transform (FFT) as discussed by the authors has been used in a variety of applications, such as matching filtering, detection, and pattern recognition, as well as signal recovery.
Abstract: Preface. Acknowledgments. Introduction. Signals, Systems, and Transformations. Wigner Distributions and Linear Canonical Transforms. The Fractional Fourier Transform. Time-Order and Space-Order Representations. The Discrete Fractional Fourier Transform. Optical Signals and Systems. Phase-Space Optics. The Fractional Fourier Transform in Optics. Applications of the Fractional Fourier Transform to Filtering, Estimation, and Signal Recovery. Applications of the Fractional Fourier Transform to Matched Filtering, Detection, and Pattern Recognition. Bibliography on the Fractional Fourier Transform. Other Cited Works. Credits. Index.
1,287 citations
TL;DR: An algorithm for efficient and accurate computation of the fractional Fourier transform for signals with time-bandwidth product N, which computes the fractionsal transform in O(NlogN) time.
Abstract: An algorithm for efficient and accurate computation of the fractional Fourier transform is given. For signals with time-bandwidth product N, the presented algorithm computes the fractional transform in O(NlogN) time. A definition for the discrete fractional Fourier transform that emerges from our analysis is also discussed.
1,034 citations
TL;DR: This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite-Gaussian functions, and is exactly unitary, index additive, and reduces to the D FT for unit order.
Abstract: We propose and consolidate a definition of the discrete fractional Fourier transform that generalizes the discrete Fourier transform (DFT) in the same sense that the continuous fractional Fourier transform generalizes the continuous ordinary Fourier transform. This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite-Gaussian functions. The definition is exactly unitary, index additive, and reduces to the DFT for unit order. The fact that this definition satisfies all the desirable properties expected of the discrete fractional Fourier transform supports our confidence that it will be accepted as the definitive definition of this transform.
604 citations
01 Aug 1972
TL;DR: The groundwork is set through a discussion of the relationship between the binary representation of numbers and truncation or rounding, and a formulation of a statistical model for arithmetic roundoff, to illustrate techniques of working with particular models.
Abstract: When digital signal processing operations are implemented on a computer or with special-purpose hardware, errors and constraints due to finite word length are unavoidable. The main categories of finite register length effects are errors due to A/D conversion, errors due to roundoffs in the arithmetic, constraints on signal levels imposed by the need to prevent overflow, and quantization of system coefficients. The effects of finite register length on implementations of linear recursive difference equation digital filters, and the fast Fourier transform (FFT), are discussed in some detail. For these algorithms, the differing quantization effects of fixed point, floating point, and block floating point arithmetic are examined and compared. The paper is intended primarily as a tutorial review of a subject which has received considerable attention over the past few years. The groundwork is set through a discussion of the relationship between the binary representation of numbers and truncation or rounding, and a formulation of a statistical model for arithmetic roundoff. The analyses presented here are intended to illustrate techniques of working with particular models. Results of previous work are discussed and summarized when appropriate. Some examples are presented to indicate how the results developed for simple digital filters and the FFT can be applied to the analysis of more complicated systems which use these algorithms as building blocks.
333 citations
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