The multiple-parameter fractional Fourier transform

The fractional Fourier transform （FRFT） has multiplicity, which is intrinsic in fractional operator. A new source for the multiplicity of the weight-type fractional Fourier transform （WFRFT） is proposed, which can generalize the weight coefficients of WFRFT to contain two vector parameters m,n ∈ Z^M . Therefore a generalized fractional Fourier transform can be defined, which is denoted by the multiple-parameter fractional Fourier transform （MPFRFT）. It enlarges the multiplicity of the FRFT, which not only includes the conventional FRFT and general multi-fractional Fourier transform as special cases, but also introduces new fractional Fourier transforms. It provides a unified framework for the FRFT, and the method is also available for fractionalizing other linear operators. In addition, numerical simulations of the MPFRFT on the Hermite-Gaussian and rectangular functions have been performed as a simple application of MPFRFT to signal processing.     

Digital computation of the weighted-type fractional Fourier transform

The paper reveals the time-frequency symmetric property of the weighted-type fractional Fourier transform (WFRFT) by investigating the original definition of the WFRFT, and proposes a discrete algorithm of the WFRFT based on the weighted discrete Fourier transform (WDFT) algorithm with constraint conditions of the definition of the WFRFT and time-domain sampling. When the WDFT is considered in digital computation of the WFRFT, the Fourier transform in the definition of the WFRFT should be defined in frequency (Hz) but not angular frequency (rad/s). The sampling period Δt and sampling duration T should satisfy Δt = T/N = 1/N(1/2) when N-point DFT is utilized. Since Hermite-Gaussian functions are the best known eigenfunctions of the fractional Fourier transform (FRFT), digital computation based on eigendecomposition is also carried out as the additional verification and validation for the WFRFT calculation.     

Research progress of the fractional Fourier transform in signal processing

While solving a heat conduction problem in 1807, a French scientist Jean Baptiste Jo-seph Fourier, suggested the usage of the Fourier theorem. Thereafter, the Fourier trans-form (FT) has been applied widely in many scientific disciplines, and has played i…     

基于分数阶Fourier变换的数字图像加密算法研究*

基于分数阶Fourier变换和混沌,提出了一种数字图像加密方法。具体算法为：先对图像进行混沌置乱,再进行X方向的离散分数阶Fourier变换;然后在分数阶Fourier域内作混沌置乱,再进行Y方向的离散分数阶Fourier变换;最后将加密图像的实部与虚部映射到RGB,形成可传输的彩色加密图像。实验结果表明,该加密算法具有很好的安全性,在信息安全领域有较好的应用前景和研究价值。     

分数阶Fourier变换在多分量信号谱图分析中的应用

作为时频分析方法的一种,谱图对多分量信号分析时受交叉项影响,特别是当信号相隔很近时尤为严重,而且频率分辨率会受影响。给出了结合分数阶Fourier变换（FrFT）对多分量信号进行谱图分析的方法。首先利用分数阶二阶矩极值点而找到相应的最优旋转阶数,对所给多分量信号按此阶数做分数阶Fourier变换,再在此基础上做谱图分析。仿真实例表明,该方法对初始频率、调频率很接近的多分量的chirp信号能有效识别,交叉项可得到较好的抑制。     

分数阶傅里叶变换对OFDM系统峰均比的影响

分析并讨论了分数阶傅里叶变换对OFDM系统峰均功率比性能的影响,并与传统的采用离散傅里叶变换的OFDM系统的峰均功率比性能进行了比较。结果表明在子载波数较少的情况下,采用分数阶傅里叶变换的OFDM系统的峰均功率比性能要优于采用离散傅里叶变换的OFDM系统的峰均功率比性能,而随着系统子载波数量的逐渐增加,二者性能趋向一致。     

Fractional Fourier domain analysis of decimation and interpolation

The sampling rate conversion is always used in order to decrease computational amount and storage load in a system. The fractional Fourier transform (FRFT) is a powerful tool for the analysis of nonstationary signals, especially, chirp-like signal. Thus, it has become an active area in the signal processing community, with many applications of radar, communication, electronic warfare, and information security. Therefore, it is necessary for us to generalize the theorem for Fourier domain analysis of decimation and interpolation. Firstly, this paper defines the digital fre- quency in the fractional Fourier domain (FRFD) through the sampling theorems with FRFT. Secondly, FRFD analysis of decimation and interpolation is proposed in this paper with digital frequency in FRFD followed by the studies of interpolation filter and decimation filter in FRFD. Using these results, FRFD analysis of the sam- pling rate conversion by a rational factor is illustrated. The noble identities of decimation and interpolation in FRFD are then deduced using previous results and the fractional convolution theorem. The proposed theorems in this study are the bases for the generalizations of the multirate signal processing in FRFD, which can advance the filter banks theorems in FRFD. Finally, the theorems introduced in this paper are validated by simulations.     

二维离散分数阶Fourier变换的双混沌图像加密算法

针对现今分数阶Fourier变换和传统混沌加密的不足,提出了一种基于二维离散分数阶Fourier变换的双混沌图像加密算法。该算法首先借助明文图像信息生成辅助密钥矩阵与输入密钥相结合得到混沌序列,再将生成的中间密文作为二维离散分数阶Fourier变换输入,最后进行置乱操作,使得明文信息得到很好的隐藏。通过实验仿真表明,该算法不仅能有效抵抗统计特征攻击、差分攻击,而且大大改善经传统分数阶Fourier变换后直方图像不平滑的缺点,达到很好的加密效果。     

A fusion estimation method based on fractional Fourier transform

Image denoising methods have different denoising performance in both spatial and transform domains, and each method has its relative advantages and inherent shortcomings compared with other methods. A very intuitive idea is to find that an effective fusion method that can combine with the advantages of different denoising methods. In this paper, we propose a novel fusion method based on the fractional Fourier transform and apply it to image denoising problem. Our method is mainly divided into three steps: Firstly, a pre-estimation is made by any two denoising method separately in the spatial domain. Secondly, using these two estimated results as well as their Fourier transform, twice Fourier transform and three times Fourier transform, we obtain a fused result in the fractional Fourier transform domain. Thirdly, the inverse fractional Fourier transform and the modulus operation are used to obtain the final fusion result. Obviously, this approach is the fusion method in four different domains. Experimental results on benchmark test images demonstrate that the proposed method outperforms state-of-the-art stand-alone methods as: BM3D, DDID, MLP, EPLL and also superior to the fusion methods such as classic wavelet fusion method, PCA fusion method and the state-of-the-art CIEM fusion method in terms of quantity value such as the peak signal to noise ratio (PSNR), the structural similarity (SSIM), and visual quality.     

MIMO-OFDM system based on fractional Fourier transform and selecting algorithm for optimal order

In the rapidly time-varying channel environment, the performance of traditional MIMO-OFDM system is deteriorated due to the intercarrier interference. In this paper, a novel MIMO-OFDM system is proposed, in which the modulation and de- modulation of the symbols are implemented by the fractional Fourier transform instead of traditional Fourier transform. Through selecting the optimal order of the fractional Fourier transform, the modulated signals can match the time-varying channel characteristics, which results in a mitigation of the intercarrier interference. Furthermore, an algorithm is presented for selecting the optimal order of fractional Fourier transform, and the impact of system parameters on the optimal order is analyzed. Simulation results show that the proposed system can concentrate the power of desired signal effectively and improve the performance over rapidly time-varying channels with respect to the traditional MIMO-OFDM system.     

Research on the application of 4-weighted fractional Fourier transform in communication system

The paper reveals the relationship between the weighting coefficients and weighted functions via the research of coefficients matrix and based on the original definition of 4-weighted fractional Fourier transform(4-WFRFT).The multi-parameters expression of weighting coefficients are given.Moreover, the 4-WFRFT of discrete sequences is defined by introducing DFT into it, which makes it suitable for digital communication systems.After analyzing the properties of WFRFT, a typical scheme for modulation/demodula...     

基于快速分数阶傅氏变换的DDoS攻击检测

针对传统检测方法存在精度低、训练复杂度高、适应性差的问题,提出了基于快速分数阶Fourier变换估计Hurst指数的DDoS攻击检测方法。利用DDoS攻击对网络流量自相似性的影响,通过监测Hurst指数变化阈值判断是否存在DDoS攻击。在DARPA2000数据集和不同强度TFN2K攻击流量数据集上进行了DDoS攻击检测实验,实验结果表明,基于FFrFT的DDoS攻击检测方法有效,相比于常用的小波方法,该方法计算复杂度低,实现简单,Hurst指数估计精度更高,能够检测强度较弱的DDoS攻击,可有效降低漏报、误报率。     

The fractional Fourier transform: theory, implementation and error analysis

The fractional Fourier transform is a time–frequency distribution and an extension of the classical Fourier transform. There are several known applications of the fractional Fourier transform in the areas of signal processing, especially in signal restoration and noise removal. This paper provides an introduction to the fractional Fourier transform and its applications. These applications demand the implementation of the discrete fractional Fourier transform on a digital signal processor (DSP). The details of the implementation of the discrete fractional Fourier transform on ADSP-2192 are provided. The effect of finite register length on implementation of discrete fractional Fourier transform matrix is discussed in some detail. This is followed by the details of the implementation and a theoretical model for the fixed-point errors involved in the implementation of this algorithm. 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.     

基于分数傅里叶变换的二维工程图水印算法

提出一种基于离散分数傅里叶变换（DFRFT）的二维工程图数字水印算法。该算法分块提取工程图中线段的相对坐标线构造复值信号量,将水印嵌入复值信号量的分数傅里叶变换频谱（FRT）中。实验表明,该算法对平移、旋转、缩放、部分实体删除或添加等攻击具有良好的鲁棒性,同时具有良好的安全性。     

Constraining error—A sliding discrete Fourier transform investigation

The sliding discrete Fourier transform provides an alternative to the FFT, permitting a custom choice of frequency decomposition which outputs an update after each input sample. The technique relies on the application of the Fourier shift property, and is recursive by nature. This work investigates the error performance of alternative techniques (SDFT; gSDFT; mSDFT, rSDFT; and Douglas and Soh algorithms) under both floating point and fixed point arithmetic constraints. The results highlight that the sliding discrete Fourier transform with error correction provides consistent error performance over a range of test cases, and indicates the limitations applicable to all techniques.     

基于分数阶傅里叶变换的多项式相位信号参数估计

研究了一种基于分数阶傅里叶变换(FRFT)的多项式相位信号快速估计方法,对于线性调频信号(LFM),即用信号延时相关解调的方法得到调频斜率的粗略估计,从而得到分数阶旋转角度的范围,简化为小范围的一维搜索问题。多项式相位信号的检测通过延时相关解调可转化为LFM信号的检测,再运用FRFT便可进行参数估计。理论分析与仿真结果表明该方法简单,估计性能好。     

离散分数阶Fourier变换的高速FPGA实现

介绍了一种直接在时域和频域对信号进行离散的数值计算方法.针对工程实际,提出了一种基于FPGA的硬件实现方法,同时给出了具体的算法和计算机仿真结果.     

量子混沌和分数阶Fourier变换的图像加密算法

针对传统的自然混沌系统安全性低的问题,提出了量子混沌和分数阶Fourier变换的图像加密算法。通过引入量子Logistic混沌映射,解决了Logistic映射存在的周期窗口、伪随机和非周期性不好等缺陷,还改善了计算机进行浮点数运算丢失精度的问题。同时将混沌系统和分数阶Fourier变换相结合,实现了介于空间域和频域的分数域置乱,克服了传统一些方法只在单一域变换和单纯使用某一种方案而导致参数变量少,系统结构简单,直方图不均匀等缺点。实验和仿真结果表明,该算法具有密钥空间大,计算复杂度低,敏感性强等优点,能够有效地抵御统计分析攻击。     

Recursive sliding discrete Fourier transform with oversampled data

The Discrete Fourier Transform (DFT) has played a fundamental role for signal analysis. A common application is, for example, an FFT to compute a spectral decomposition, in a block by block fashion. However, using a recursive, discrete, Fourier transform technique enables sample-by-sample updating, which, in turn, allows for the computation of a fine time–frequency resolution. An existing spectral output is updated in a sample-by-sample fashion using a combination of the Fourier time shift property and the difference between the most recent input sample and outgoing sample when using a window of finite length. To maintain sampling-to-processing synchronisation, a sampling constraint is enforced on the front–end hardware, as the processing latency per input sample will determine the maximum sampling rate. This work takes the recursive approach one step further, and enables the processing of multiple samples acquired through oversampling, to update the spectral output. This work shows that it is possible to compute a fine-grained spectral decomposition while increasing usable signal bandwidths through higher sampling rates. Results show that processing overhead increases sub-linearly, with signal bandwidth improvement factors of up to 6.7× when processing 8 samples per iteration.