1-D Directional Filter Based Texture Descriptor in Fractional Fourier Domain

Texture analysis is a fundamental field in computer vision. However, it is also a particularly difficult problem for no universal mathematical model of real world textures. By extending a new application of the fractional Fourier transform (FrFT) in the field of texture analysis, this paper proposes an FrFT-based method for describing textures. Firstly, based on the Radon-Wigner transform, 1-D directional FrFT filters are designed to two types of texture features, i.e., the coarseness and directionality. Then, the frequencies with maximum and median amplitudes of the FrFT of the input signal are regarded as the output of the 1-D directional FrFT filter. Finally, the mean and the standard deviation are used to compose of the feature vector. Compared to the WD-based method, three benefits can be achieved with the proposed FrFT-based method, i.e., less memory size, lower computational load, and less disturbed by the cross-terms. The proposed method has been tested on 16 standard texture images. The experimental results show that the proposed method is superior to the popular Gabor filtering-based method.     

基于分数阶傅里叶变换的LFM干扰抑制算法

针对利用分数阶傅里叶变换(fractional Fourier transform,FrFT)进行干扰抑制时门限阈值设定的难题,为有效抑制DS/FH系统中的线性调频(linear frequency modulated,LFM)干扰,通过深入分析DS/FH和LFM信号的FrFT性质,提出了一种新的LFM干扰抑制算法,该算法充分利用FrFT时频面坐标轴旋转性质,通过计算信号的不同阶次FrFT并搜索其峰值,确定信号最优FrFT阶次,完成对LFM干扰信号的判别,最后采用频域陷波来抑制LFM干扰。理论分析和仿真实验表明,该算法能够有效的剔除DS/FH系统中的LFM干扰信号,不需要阈值门限设定,算法具有较好的稳健性和普适性。     

基于最优分数阶傅里叶变换MIMO-OFDM系统

在快速时变无线信道环境下,由于子载波间干扰(ICaI)和小区间干扰(ICeI)的存在,使得传统MIMO-OFDM系统性能有很大的下降.为了减小干扰,考虑每个用户可以根据周围的干扰环境选择合适的分数阶傅里叶变换(FrFT)来调制和解调信号.提出了基于FrFT的MIMO-OFDM系统模型以及基于此模型的干扰抑制合并(IRC)均衡器.由于实际通信系统中HARQ技术的运用,使得系统的初传误码率并不要求很低,所以提出了FrFT阶数优化准则,即在接收符号信噪比满足门限要求的前提下使得系统容量最大化,由此可以得到基于最优FrFT的IRC均衡器.最后系统仿真结果表明这种优化准则是有效的,在干扰得到抑制的同时,通过更新最优化阶数还可以使得小区吞吐量也有明显的提高.     

基于AF切片与FrFT的STLFM信号检测与参数估计算法

针对基于Radon-Ambiguity变换(RAT)和分数阶Fourier变换(FrFT)的对称三角线性调频(STLFM)信号参数估计算法运算量较大的问题,在分析RAT变换估计调频斜率和STLFM信号模糊函数特征基础上提出了一种基于模糊函数(AF)切片和FrFT的STLFM信号参数估计新算法。该算法首先通过对观测信号固定时延点AF切片的计算估计出调频斜率,然后得到相应的FrFT最优变换阶数,最后利用两次FrFT估计出STLFM信号的调频带宽、调频周期和中心频率等参数。整个算法只需3次FFT的计算量即可估计出STLFM信号的所有参数。仿真实验表明了该算法能实现STLFM信号参数的快速有效估计。     

基于矩阵加权多模型融合的认知跟踪波形设计

针对机动目标状态跟踪问题,认知雷达能够调整发射端波形来获取持续、稳健目标跟踪信息.本文基于矩阵加权多模型融合思想引入一种新的面向机动目标跟踪的认知雷达自适应波形设计方法(Adaptive waveform design method based on Matrix-weighted Interacting Multiple Model,AMIMM).首先,利用多模型思路对机动目标状态进行建模,并考虑各模型目标状态估计及其误差协方差矩阵中元素间相关性,以矩阵加权融合方式代替传统概率加权方式,进而构造基于矩阵加权多模型信息融合的跟踪算法框架;然后,以多模型状态融合后的状态估计误差协方差矩阵为基准,利用特征值分解(Eigen Value Decomposition,EVD)技术求取融合后状态估计误差协方差矩阵对应椭圆参数;最后,通过分数阶傅里叶变换(fractional Fourier transform,FrFT)来旋转雷达量测误差椭圆,使得量测误差椭圆与融合后目标状态估计误差椭圆正交,从而获得下一时刻认知波形参数,实现波形自适应捷变.仿真实验表明,与当前流行多种算法相比,本文所提算法能够进一步提高机动目标跟踪精度和稳健性.     

基于广义随机共振的Chirp信号检测性能分析

为便于提取多个chirp信号叠加噪声模型中目标chirp信号的参数信息,本文研究了该混合信号激励过阻尼双稳系统的随机共振现象。但由于chirp信号在频域能量的非聚焦性,传统刻画随机共振的信噪比指标失效。因此,基于chirp信号在分数阶傅里叶变换域上的能量分布特性,对非线性随机动力系统的畸变输出信号进行变换域上的最优滤波,给出最优分数阶傅里叶变换域上的信干比定义形式,并以此作为目标chirp信号经过系统后的性能增益的度量和广义随机共振现象的判别依据。数值仿真结果表明,在存在多个chirp信号驱动的过阻尼双稳系统中,当目标chirp信号或噪声能量不足以激发粒子跃迁时,干扰chirp信号能在一定程度上协助粒子发生跃迁,产生协同随机共振现象,从而提高对目标chirp信号的检测性能。     

基于自适应线调频高斯基展开的参数识别方法

提出了用自适应线调频高斯基展开及其自适应谱进行参数识别的时频分析方法.用线调频高斯基函数自适应地对响应信号进行匹配分解,得到信号的线调频高斯基展开及其自适应谱.根据自适应谱从响应信号中重构出具有单频特性的振动信号,再由单自由度系统的参数识别方法识别系统的模态参数.并针对一多自由度系统具体论述了固有频率、阻尼比和振型的识别方法,识别结果与理论结果吻合较好.该方法不需要输入信号,并对平稳、非平稳信号均适用,且具有较强的抗噪声能力.算例分析说明提出的方法是参数识别的有效方法之一.     

(研究生论坛)IEEE802.11g无线局域网中基于FrFT角分多址的多流OFDM技术

为了提高数据传输速率,本文把分数阶傅立叶变换（FrFT）角度看作一种时频联合资源,通过设计最优分配准则提出IEEE802.11g无线局域网（WLAN）中基于FrFT角分多址（ADMA）的多流正交频分复用（OFDM）技术。发送端通过不同角度的逆分数阶傅立叶变换（IFrFT）对多个数据流进行调制,由于每个数据流中不同子信道上调制基函数的相互正交性,以及不同数据流之间相同子信道上调制基函数的近似正交性,接收端可以通过FrFT域相关检测很好地抑制各数据流间干扰。最后的仿真结果表明：相比传统IEEE802.11g中单流OFDM技术,系统传输速率可以得到显著提高。     

单通道彩色图像加密方案

设计了一种基于三相位编码的单通道彩色图像加密方法。将彩色图像转换到HSI空间,I分量即可作为相位编码的原始待加密图像;H分量可作为第1个相位,采用双图像加密算法对S分量加密后得到的相位,与H分量构成了对I分量加密的2个相位,第3个相位密钥是S分量加密后得到的相位与变换输出的相位之差。由于基于分数傅里叶变换(FrFT)的双随机相位编码技术具有很高的安全性,在增加一重相位编码后,进一步提高了安全性,在不知道密钥的情况下几乎不可能获得原图像信息,由此保证了彩色图像加密的安全性。模拟实验结果验证了该方法的有效性和安全性。给出了光学实现方案,基于该算法设计了多幅彩色图像的加密方案。     

基于模糊函数切片和 FrFT 的快速 LFM 信号参数估计

RAT（Radon-Ambiguity Transform）和FrFT（Fractional Fourier Transform）被广泛应用于线性调频（LFM）信号的参数估计。针对RAT调频斜率估计时的全角度域搜索和FrFT参数估计时的两维搜索导致计算量增加的问题,提出了一种快速LFM信号参数估计方法。采用模糊函数（Ambiguity Function）切片和RAT相结合的方法快速估计调频斜率,并以此确定最优分数阶变换阶数,然后利用一次FrFT实现LFM信号的剩余参数估计。理论分析和实验仿真表明了该方法的有效性。     

化验室资源调度优化的改进型克隆选择算法

为了提升化验室处理化验单能力,实现资源调度优化,建立了化验室调度模型,引入了克隆选择算子、自适应变异算子以及多种群协同进化思想,提出了改进型克隆选择算法,并运用该算法对化验室处理化验单进行了调度优化。将改进型克隆选择算法与多种类型算法进行对比,结果显示,改进型克隆选择算法能有效改善早熟收敛问题,提高搜索效率,获得最优分配方案,适用于化验室化验单调度问题,满足实际要求。     

基于移动最小二乘曲线拟合的LFM信号参数估计

针对二维搜索方法实现基于分数阶傅里叶变换(FrFT)的线性调频信号(LFM)参数估计中存在的峰值估计偏差问题,提出可利用移动最小二乘(MLS)技术来拟合FrFT模函数峰值检测向量的方法以提高参数估计性能.该算法将复杂的二维搜索问题简化至一维曲线拟合来进行处理.理论分析与仿真结果表明,该方法在保留分数阶域处理优点的同时,可明显减少运算量,并达到较高估计精度,为基于FrFT的LFM信号实时检测提供了可能.     

基于DPCA-FrFT算法的SAR运动目标参数估计

在双通道的工作模式下,结合DPCA技术和分数阶傅立叶变换实现了对SAR运动目标的定位和两个方向(方位向和地面距离向)上速度的估计。首先建立了双通道SAR系统对运动目标的回波信号模型,阐述了动目标DPCA信号为线性调频信号这个特点,并且根据这个特点采用了分数阶傅立叶变换对其进行检测和参数估计。由于DPCA技术具有良好的杂波对消功能以及FrFT对chirp信号的良好检测效果,在不同信噪比情况下,DPCA-FrFT算法都能有效地抑制静止背景、获得良好的测速和定位效果。仿真结果证明了该算法的正确性和有效性。     

Research Progress of the Sampling Theorem Associated with the Fractional Fourier Transform

Sampling is a bridge between continuous-time and discrete-time signals, which is important to digital signal processing. The fractional Fourier transform(FrFT) that serves as a generalization of the FT can characterize signals in multiple fractional Fourier domains, and therefore can provide new perspectives for signal sampling and reconstruction. In this paper, we review recent developments of the sampling theorem associated with the FrFT, including signal reconstruction and fractional spectral analysis of uniform sampling, nonuniform samplings due to various factors, and sub-Nyquist sampling, where bandlimited signals in the fractional Fourier domain are mainly taken into consideration. Moreover, we provide several future research topics of the sampling theorem associated with the FrFT.     

基于FrFT变换和全变分正则化的异常检测算法

针对低秩稀疏表示的高光谱异常检测算法中背景字典易被污染、空间信息利用不足的问题,提出基于分数阶傅里叶变换（FrFT）和全变分正则化约束的高光谱图像异常检测算法. 通过聚类算法,将图像高维数据映射至多个子空间;构造FrFT-RX算子,增大背景和异常的可分性,得到较纯净的背景字典. 为了表示FrFT变换后中间域内背景与异常的空间特征,在低秩稀疏表示模型中引入全变分正则化项约束. 采用交替方向乘子法对模型进行优化求解,得到异常检测的结果. 在3个真实高光谱数据上开展目标检测实验,实验结果表明,与其他5种异常检测算法相比,本文算法具有更高的检测率和较低的虚警率.     

在分数傅立叶域上进行的超宽带同步捕获

针对在低信噪比情况下超宽带时域同步捕获的精度会明显下降的问题,考虑在分数域上进行同步捕获及采用域滤波的方法将四项加权的分数傅立叶定义应用在同步捕获中,其优点是可利用FFT的快速算法,且算法简单易行。利用分数域上兼顾时域和频域信息及随着分数阶增高高斯白噪声幅度逐渐平坦的特性,进行分数域上的相关捕获及分数域滤波。实验结果表明,在低信噪比时,分数域的同步捕获精度比时域上的更高。     

Curvelet Transform-Based Denoising Method for Doppler Frequency Extraction

A novel image denoising method based on curvelet transform is proposed in order to improve the performance of Doppler frequency extraction in low signal-noise-ratio (SNR) environment. The echo can be represented as a gray image with spectral intensity as     

分数阶自相关和FrFT的LFM信号参数估计

基于分数阶自相关和分数阶傅里叶变换的特点,提出了一种LFM信号检测与参数估计方法。相对分数阶傅里叶二维扫描法和匹配傅里叶变换,所提方法将检测与参数估计的二维搜索变为一维搜索,快速实现信号检测和参数估计,在多分量LFM信号情况下借助"Clean"的方法来抑制强分量对弱分量的干扰。计算机仿真表明了该算法在低信噪比多分量LFM信号检测与参数估计中的有效性。     

Key Frame Extraction of Surveillance Video Based on Fractional Fourier Transform

With the vigorous development of national infrastructure construction and public information construction, video surveillance systems have gradually penetrated various fields. The current key frame extraction technology has inadequate target details and inaccurate judgment of local actions. Addressing this problem, a key frame extraction method based on fractional Fourier transform is proposed. This method obtained the phase spectra information of different orders by performing fractional Fourier transform on the surveillance video frames. Next, the method designed an adaptive algorithm based on the golden section point to select the transformation order.Then, the phase spectrum information of two adjacent frames was used to characterize the changes in the global and local motion states of the target. The final step was to extract key frames based on this. Experimental results show that, compared with the previous methods, the key frames extracted by the method proposed in this paper can correctly capture the changes in the global and local motion states of the target.     

Chirp-Rate Resolution of Fractional Fourier Transform in Multi-component LFM Signal

Distinguishing close chirp-rates of different linear frequency modulation (LFM) signals under concentrated and complicated signal environment was studied. Firstly, detection and parameter estimation of multi-component LFM signal were used by discrete fast fractional Fourier transform (FrFT). Then the expression of chirp-rate resolution in fractional Fourier domain (FrFD) was deduced from discrete normalize time-frequency distribution, when multi-component LFM signal had only one center frequency. Furthermore, the detail influence of the sampling time, sampling frequency and chirp-rate upon the resolution was analyzed by partial differential equation. Simulation results and analysis indicate that increasing the sampling time can enhance the resolution, but the influence of the sampling frequency can be omitted. What’s more, in multi-component LFM signal, the chirp-rate resolution of FrFT is no less than a minimal value, and it mainly dependent on the biggest value of chirp-rates, with which it has an approximately positive exponential relationship.