Discrete fractional Fourier transform based on orthogonalprojections

The continuous fractional Fourier transform (FRFT) performs a spectrum rotation of signal in the time-frequency plane, and it becomes an important tool for time-varying signal analysis. A discrete fractional Fourier transform has been developed by Santhanam and McClellan (see ibid., vol.42, p.994-98, 1996) but its results do not match those of the corresponding continuous fractional Fourier transforms. We propose a new discrete fractional Fourier transform (DFRFT). The new DFRFT has DFT Hermite eigenvectors and retains the eigenvalue-eigenfunction relation as a continous FRFT. To obtain DFT Hermite eigenvectors, two orthogonal projection methods are introduced. Thus, the new DFRFT will provide similar transform and rotational properties as those of continuous fractional Fourier transforms. Moreover, the relationship between FRFT and the proposed DFRFT has been established in the same way as the conventional DFT-to-continuous-Fourier transform     

Uncertainty principles for discrete signals associated with the fractional Fourier and linear canonical transforms

The fractional Fourier transform (FRFT), which generalizes the classical Fourier transform, has gained much popularity in recent years because of its applications in many areas, including optics, radar, and signal processing. There are relations between duration in time and bandwidth in fractional frequency for analog signals, which are called the uncertainty principles of the FRFT. However, these relations are only suitable for analog signals and have not been investigated in discrete signals. In practice, an analog signal is usually represented by its discrete samples. The purpose of this paper is to propose an equivalent uncertainty principle for the FRFT in discrete signals. First, we define the time spread and the fractional frequency spread for discrete signals. Then, we derive an uncertainty relation between these two spreads. The derived results are also extended to the linear canonical transform, which is a generalized form of the FRFT.     

Relations between fractional operations and time-frequencydistributions, and their applications

The fractional Fourier transform (FRFT) is a useful tool for signal processing. It is the generalization of the Fourier transform. Many fractional operations, such as fractional convolution, fractional correlation, and the fractional Hilbert transform, are defined from it. In fact, the FRFT can be further generalized into the linear canonical transform (LCT), and we can also use the LCT to define several canonical operations. In this paper, we discuss the relations between the operations described above and some important time-frequency distributions (TFDs), such as the Wigner distribution function (WDF), the ambiguity function (AF), the signal correlation function, and the spectrum correlation function. First, we systematically review the previous works in brief. Then, some new relations are derived and listed in tables. Then, we use these relations to analyze the applications of the FRPT/LCT to fractional/canonical filter design, fractional/canonical Hilbert transform, beam shaping, and then we analyze the phase-amplitude problems of the FRFT/LCT. For phase-amplitude problems, we find, as with the original Fourier transform, that in most cases, the phase is more important than the amplitude for the FRFT/LCT. We also use the WDF to explain why fractional/canonical convolution can be used for space-variant pattern recognition     

Closed-form discrete fractional and affine Fourier transforms

The discrete fractional Fourier transform (DFRFT) is the generalization of discrete Fourier transform. Many types of DFRFT have been derived and are useful for signal processing applications. We introduce a new type of DFRFT, which are unitary, reversible, and flexible; in addition, the closed-form analytic expression can be obtained. It works in performance similar to the continuous fractional Fourier transform (FRFT) and can be efficiently calculated by the FFT. Since the continuous FRFT can be generalized into the continuous affine Fourier transform (AFT) (the so-called canonical transform), we also extend the DFRFT into the discrete affine Fourier transform (DAFT). We derive two types of the DFRFT and DAFT. Type 1 is similar to the continuous FRFT and AFT and can be used for computing the continuous FRFT and AFT. Type 2 is the improved form of type 1 and can be used for other applications of digital signal processing. Meanwhile, many important properties continuous FRFT and AFT are kept in the closed-form DFRFT and DAFT, and some applications, such as filter design and pattern recognition, are also discussed. The closed-form DFRFT we introduce has the lowest complexity among all current DFRFTs that is still similar to the continuous FRFT     

Fractional cosine, sine, and Hartley transforms

In previous papers, the Fourier transform (FT) has been generalized into the fractional Fourier transform (FRFT), the linear canonical transform (LCT), and the simplified fractional Fourier transform (SFRFT). Because the cosine, sine, and Hartley transforms are very similar to the FT, it is reasonable to think they can also be generalized by the similar way. We introduce several new transforms. They are all the generalization of the cosine, sine, or Hartley transform. We first derive the fractional cosine, sine, and Hartley transforms (FRCT/FRST/FRHT). They are analogous to the FRFT. Then, we derive the canonical cosine and sine transforms (CCT/CST). They are analogous to the LCT. We also derive the simplified fractional cosine, sine, and Hartley transforms (SFRCT/SFRST/SFRHT). They are analogous to the SFRFT and have the advantage of real-input-real-output. We also discuss the properties, digital implementation, and applications (e.g., the applications for filter design and space-variant pattern recognition) of these transforms. The transforms introduced in this paper are very efficient for digital implementation. We can just use one half or one fourth of the real multiplications required for the FRFT and LCT to implement them. When we want to process even, odd, or pure real/imaginary functions, we can use these transforms instead of the FRFT and LCT. Besides, we also show that the FRCT/FRST, CCT/CST, and SFRCT/SFRST are also useful for the one-sided (t ∈ [0, ∞]) signal processing     

Generalized convolution and product theorems associated with linear canonical transform

The linear canonical transform (LCT), which is a generalized form of the classical Fourier transform (FT), the fractional Fourier transform (FRFT), and other transforms, has been shown to be a powerful tool in optics and signal processing. Many results of this transform are already known, including its convolution theorem. However, the formulation of the convolution theorem for the LCT has been developed differently and is still not having a widely accepted closed-form expression. In this paper, we first propose a generalized convolution theorem for the LCT and then derive a corresponding product theorem associated with the LCT. The ordinary convolution theorem for the FT, the fractional convolution theorem for the FRFT, and some existing convolution theorems for the LCT are shown to be special cases of the derived results. Moreover, some applications of the derived results are presented.     

Generalized convolution theorem associated with fractional Fourier transform

The fractional Fourier transform (FRFT)—a generalization of the well‐known Fourier transform (FT)—is a comparatively new and powerful mathematical tool for signal processing. Many results in Fourier analysis have currently been extended to the FRFT, including the ordinary convolution theorem. However, the extension of the ordinary convolution theorem associated with the FRFT has been developed differently and is still not having a widely accepted closed‐form expression. In this paper, a generalized convolution theorem for the FRFT is proposed, and the dual of it is also presented. The ordinary convolution theorem and some of its existing extensions related to the FRFT are shown to be special cases of the derived results. Moreover, some applications of the derived results are presented. Copyright © 2012 John Wiley & Sons, Ltd.     

一种新的变换域自适应LMS滤波算法

为了改善时变系统中的LMS算法收敛速度,一般可以在变换域进行自适应处理。通过研究和分析分数阶傅里叶变换与时-频平面的关系,提出在分数阶傅里叶变换域进行自适应时-频滤波。所提出的方法首先搜索最佳变换域,然后在分数阶傅里叶变换域进行LMS自适应滤波。仿真结果表明,与目前一些基于变换域的方法对比,新方法通过对时-频平面的旋转,可以显著加速算法收敛性。     

分数阶傅立叶变换的多种定义形式及其光学实现系统分析

分数阶傅立叶变换是经典傅立叶变换的广义形式,它同时从时间域和频率域(或空间域)揭示信号特征。本文系统地分析了分数阶傅立叶变换三种定义形式及其所对应的光学实现系统的组成和原理,说明了光学信息处理系统实现分数阶傅立叶变换的有效性.     

Multiplicative filtering in the fractional Fourier domain

In this article, we investigate the multiplicative filtering in the fractional Fourier transform (FRFT) domain based on the generalized convolution theorem which states that the convolution of two signals in time domain results in simple multiplication of their FRFTs in the FRFT domain. In order to efficiently implement multiplicative filtering, we express the generalized convolution structure by the conventional convolution operation. Utilizing the generalized convolution structure, we convert the multiplicative filtering in the FRFT domain easily to the time domain. Based on the model of multiplicative filtering in the FRFT domain, a practical method is proposed to achieve the multiplicative filtering through convolution in the time domain. This method can be realized by classical Fast Fourier transform (FFT) and has the same capability compared with the method achieved in the FRFT domain. As convolution can be performed by FFT, this method is more useful from practical engineering perspective.     

抽样率转换的分数阶Fourier域分析

为了节省计算量及存储空间,在一个信号处理系统中常常需要不同的抽样率及其相互之间的转换.而在分数阶Fourier域中分析信号完全可用较低的抽样频率来抽样(低于Nyquist抽样率),这就意味着建立在频域上的传统抽样率转换理论将不再适用.本文将建立在Fourier变换(频域)上的传统抽样率转换理论推广到了分数阶Fourier域,通过研究时域抽取和零值内插操作在分数阶Fourier域的表示及其含义,导出了基于分数阶Fourier变换的有理分数倍抽样率转换理论.可以看到,将分数阶Fourier变换的变换阶数取为π/2,便得到了与传统频域多抽样率理论完全一致的结果.最后,本文通过仿真对导出的分数阶Fourier域多抽样率理论进行了验证.     

Eigenfunctions of Fourier and Fractional Fourier Transforms With Complex Offsets and Parameters

In this paper, we derive the eigenfunctions of the Fourier transform (FT), the fractional FT (FRFT), and the linear canonical transform (LCT) with (1) complex parameters and (2) complex offsets. The eigenfunctions in the cases where the parameters and offsets are real were derived in literature. We extend the previous works to the cases of complex parameters and complex offsets. We first derive the eigenvectors of the offset discrete FT. They approximate the samples of the eigenfunctions of the continuous offset FT. We find that the eigenfunctions of the offset FT with complex offsets are the smoothed Hermite-Gaussian functions with shifting and modulation. Then we extend the results for the case of the offset FRFT and the offset LCT. We can use the derived eigenfunctions to simulate the self-imaging phenomenon for the optical system with energy-absorbing component, mode selection, encryption, and define the fractional Z-transform and the fractional Laplace transform.     

分数阶傅里叶变换域上带通信号的采样定理

傅里叶变换和采样定理是信号处理领域的两大基本问题,采样定理研究了傅里叶变换域上带限信号的采样和重构理论.分数阶傅里叶变换(FRFT)是傅里叶变换的一种推广,与之相应的采样理论目前还不十分完备,所以有必要从FRFT域上重新研究采样定理.本文首先得到了均匀冲激串采样信号的FRFT,然后在此基础上导出了FRFT域上带通信号和低通信号的采样定理和重构公式.这些结果是经典理论的推广,将丰富分数阶傅里叶变换的理论体系.     

Sampling and Sampling Rate Conversion of Band Limited Signals in the Fractional Fourier Transform Domain

The fractional Fourier transform (FRFT) has become a very active area in signal processing community in recent years, with many applications in radar, communication, information security, etc., This study carefully investigates the sampling of a continuous-time band limited signal to obtain its discrete-time version, as well as sampling rate conversion, for the FRFT. Firstly, based on product theorem for the FRFT, the sampling theorems and reconstruction formulas are derived, which explain how to sample a continuous-time signal to obtain its discrete-time version for band limited signals in the fractional Fourier domain. Secondly, the formulas and significance of decimation and interpolation are studied in the fractional Fourier domain. Using the results, the sampling rate conversion theory for the FRFT with a rational fraction as conversion factor is deduced, which illustrates how to sample the discrete-time version without aliasing. The theorems proposed in this study are the generalizations of the conventional versions for the Fourier transform. Finally, the theory introduced in this paper is validated by simulations.     

Bayesian reconstruction of signals invariant under a space-groupsymmetry from Fourier transform magnitudes

A Bayesian signal reconstruction problem, motivated by X-ray crystallography, which includes magnitude of Fourier transform measurements and a Markov random field a priori model was approximately solved by Doerschuk (see J. Opt. Soc. Amer. A, vol.8, no.8, p.1222-1232 and p.1207-1221, 1991). These ideas are extended to signals that obey a space-group symmetry, which is a crucial extension for X-ray crystallography application. Performance statistics based on simulated data are presented.     

Periodicity-induced generalized Fourier transform pair

The field radiated by an infinite periodic structure can be expressed in terms of Floquet waves (FWs), both in the frequency domain (FD) and time domain (TD) (see Felsen, L.B. and Capolino, F., IEEE Trans. Antennas Propagat., vol.48, p.921-31, 2000). A new periodicity-induced generalized Fourier transform (FT) pair is derived relating FD-FWs to TD-FWs and vice versa, based on tabulated transforms and physical conditions at infinity. The new FTs are directly related to the simple canonical problem of a line array of sequentially excited dipoles that is a basic building block for more general phased periodic structures.     

海杂波FRFT谱的多重分形特性与目标检测

本文主要研究海杂波在分数阶傅里叶变换（FRFT）域所表现出的多重分形特性及其在海杂波目标检测中的应用。由FRFT数学定义的尺度性质可推得,自相似过程在某一变换阶数下的FRFT谱在各尺度下不具有统一的自相似特性。针对这一特性,本文将多重分形理论引入到对海杂波FRFT谱的自相似结构分析中并研究FRFT域多重分形参数的影响因素,经S波段和C波段雷达实测数据验证表明,海杂波FRFT谱具有多重分形特性且FRFT域广义Hurst指数对海杂波和目标具有良好的区分能力。在此基础上,本文利用FRFT域广义Hurst指数与双参数恒虚警检测器相结合设计海杂波中目标检测方法并分析检测性能,结果表明本文所提方法相比于经典的时域分形检测方法具有较明显地性能提升。      

An Integral Transform and Its Applications in Parameter Estimation of LFM Signals

This paper proposes a new transform called simplified linear canonical transform (SLCT) that provides a new method for parameter estimation of linear frequency-modulated (LFM) chirp signals embedded in additive white Gaussian noise. The proposed transform is a linear transform and has a more succinct form as compared with the fractional Fourier transform (FRFT). The discrete SLCT with fast Fourier transform (FFT) algorithm provides a computationally fast choice for LFM signal detection or parameter estimation. Using SLCT and a clean technique, all the components of Multi-LFM signals can be estimated seriatim. Simulations illustrate that the proposed algorithm is more effective than existing ones.     

Sampling of compact signals in offset linear canonical transform domains

The offset linear canonical transform (OLCT) is the name of a parameterized continuum of transforms which include, as particular cases, the most widely used linear transforms in engineering such as the Fourier transform (FT), fractional Fourier transform (FRFT), Fresnel transform (FRST), frequency modulation, time shifting, time scaling, chirping and others. Therefore the OLCT provides a unified framework for studying the behavior of many practical transforms and system responses. In this paper the sampling theorem for OLCT is considered. The sampling theorem for OLCT signals presented here serves as a unification and generalization of previously developed sampling theorems.     

General multifractional Fourier transform method based on the generalized permutation matrix group

The paper studies the possibility of giving a general multiplicity of the fractional Fourier transform (FRFT) with the intention of combining existing finite versions of the FRFT. We introduce a new class of FRFT that includes the conventional fractional Fourier transforms (CFRFTs) and the weighted-type fractional Fourier transforms (WFRFTs) as special cases. The class is structurally well organized because these new FRFTs, which are called general multifractional Fourier transform (GMFRFTs), are related with one another by the Generalized Permutation Matrix Group (GPMG), and their kernels are related with that of CFRFTs as the finite combination by the recursion of matrix. In addition, we have computer simulations of some GMFRFTs on a rectangular function as a simple application of GMFRFTs to signal processing.