一种分数阶傅里叶变换快速算法的研究

介绍了分数阶傅里叶变换的定义,接着提出了一种分数阶傅里叶变换的快速算法,其中分数阶傅里叶变换快速算法分三步进行:线性调频信号乘法,线性调频信号卷积,另一个线性调频信号乘法,从而利用FFT来计算FRFT。这种算法思想直观,结果与连续FRFT的输出接近。最后用具体的信号作了计算机仿真,并给出Matlab仿真结果图。     

DSSS系统LFM干扰抑制技术研究

针对传统的傅里叶变换只能对平稳干扰信号具有较好的抑制能力而对非平稳的LFM干扰信号不能有效抑制,该文引入了分数阶傅里叶变换（FRFT）,在分数阶傅里叶域给出了一种LFM信号最佳旋转角度确定的算法,并利用该算法对LFM在分数阶傅里叶域进行干扰抑制。仿真结果表明,该算法能够准确确定LFM信号在分数阶傅里叶域的最佳旋转角度,...     

一种新的mc-PPS瞬时频率变化率的估计

该文在分数阶傅里叶(FRFT)计算分解的基础上,讨论了其某些步骤在信号检测中的冗余,提出了一种简化的分数阶傅里叶算法(RFRFT),详细讨论了它的几种重要性质,并结合一次相位差分法提出了乘积性RFRFT(PRFRFT)算法,实现了mc-PPS的瞬时频率变化率(IFR)估计.同时借助角度变换提高了RFRFT识别参数的分辨率.该方法运算量小,易于实现.仿真结果证实了该方法能够有效地抑制噪声和交叉项,可以适应低信噪比环境.     

分数阶傅里叶变换的快速计算新方法

本文提出了一种分数阶傅里叶变换(FRFT)高分辨(Zoom-FRFT)算法,通过设置谱区间和输出点数M,可实现任意局部谱的高分辨计算.随后,针对M很小时Zoom-FRFT运算效率低的问题,提出了基于Horner的单点快速计算(SP-FRFT)方法,并针对零点计算做出进一步简化.利用SP-FRFT可提高少量点输出时的计算效率,也可用于非均匀采样点计算.仿真结果验证了算法的有效性.     

低信噪比下LFM- PRBC信号参数的快速估计

为了解决目前算法中线性调频-伪码( LFM- PRBC)信号参数估计计算量较大的问题,提出了一种快速估计算法。该算法采用解线调与分数阶傅里叶变换( FRFT)进行参数的估计。首先对信号进行解线调估计出调频斜率的粗略值,然后由调频斜率确定旋转角,通过FRFT估计出码元宽度的粗略值。根据延时再进行解线调估计出调频斜率的精确值,再通过FRFT估计出码元宽度的精确值与起始频率。该算法不仅计算量较低,同时具有很高的估计精度与很强的抗噪性,仿真实验验证了该算法的有效性。     

基于分数阶傅里叶变换的线性 调频信号二维波达方向估计

 本文提出了一种基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)的多线性调频(Linear Frequency Modulation,LFM)信号二维波达方向(Direction of Arrival,DOA)估计方法.该方法利用FRFT对LFM信号的能量聚集特性,构造出一种新的分数阶傅里叶域的阵列信号数据模型,并利用MUSIC算法实现对多个LFM信号的二维DOA估计.仿真实验验证了算法的有效性.     

基于分数阶傅里叶变换的MIMO-TDCS抗干扰性能研究

针对建立在快速傅里叶变换(FFT)的基础上只在频域之中作变换的传统MIMO-TDCS,抗线性调频干扰(LFM)信号能力弱,无法对其有效地应对和剔除等问题,引入分数阶傅里叶变换(FRFT).首先对电磁环境采样并估计出LFM干扰信号的各个参数,然后作相应阶次的分数阶傅里叶变换,在FRFT域内剔除干扰并生成相应的MIMO-TDCS基函数,从而达到收发两端联合主动躲避LFM干扰的目的.仿真结果表明,利用FRFT对信号良好的能量聚焦特性将线性调频干扰平稳化处理,能够有效地躲避LFM干扰的影响,为MIMO-TDCS抗非平稳干扰开辟了一条新思路.     

高动态环境二进制偏移载波调制信号的捕获

针对含一阶多普勒变化率的高动态二进制偏移载波（BOC, Binary Offset Carrier）调制信号捕获精度低且运算量大的问题,本文在部分匹配滤波器结合分数阶傅里叶变换（PMF-FRFT, Partially Matched Filter-Fractional Fourier Transform）算法的基础上提出使用分数阶功率谱累积结合分级FRFT的捕获算法。该算法首先利用PMF对信号进行分段处理,实现了接收信号和本地伪码之间的快速相关。然后结合线性调频信号（LFM, Linear frequency modulation signal）在不同FRFT域内的能量聚集特性,利用分级FRFT结合分数阶功率谱累积精确确定最优阶数。最后利用FRFT在所对应的最优阶数下结合分数阶功率谱累积实现对存在一阶多普勒变化率信号的精确捕获。仿真结果表明,该算法可以减小算法运算量,缩短捕获时间。且在功率谱累积20次,伪随机码长度为1023、信噪比为-22 dB左右的时候仍然能够实现精确的捕获。      

基于分数阶傅里叶变换的低信噪比线性调频信号参数快速估计算法

针对低信噪比线性调频信号参数估计精度低且运算量大的问题,该文提出一种基于高效分数阶傅里叶变换(FRFT)和分数阶频谱4阶原点矩的快速估计算法.该算法通过判断调频斜率的正负,以确定旋转阶次所在初始区间;进而应用高效FRFT获得初始旋转阶次;最终利用分数阶频谱4阶原点矩,进一步确定搜索区间和步长,实现精准搜索,从而满足参数精度的要求.实验结果表明,该算法尤其适合用于低信噪比情况下的线性调频(LFM)信号检测与参数的准确估计,而且运算量较低.     

一种基于分数阶Fourier变换的雷达运动目标检测算法

针对雷达回波为多分量LFM信号时,时频分析存在的交叉项干扰问题,提出了一种基于分数阶Fourier变换(Fractional Fourier Transform,FRFT)的伪Wigner分布(PWD).该方法通过在参数平面按阈值进行峰值搜索确定变换域阶次,再在相应的分数阶Fourier域计算PWD,有效地抑制了交叉项的干扰,有利于更好地提取信号的时频信息.仿真实验证明了在强背景噪声下该算法的有效性.     

Acquisition algorithm for BOC signals in high dynamic environment

As the synchronization of binary offset carrier (BOC) signals couldn’t be realized in high dynamic environment by traditional acquisition algorithms,an acquisition algorithm based on fractional Fourier transform (FRFT) and discrete polynomial-phase transform (DPT) was proposed.Firstly,the algorithm determined how to process the received signal according to the dynamic order obtained by the order operation.And then the acquisition was achieved by searching the spectral peak of the FRFT algorithm to obtain the estimation of dynamic parameters and code phase.Theoretical analysis and simulations show that the proposed algorithm eliminates the influence of second-order doppler shift rate based on original FRFT acquisition algorithm,which can successfully capture high dynamic BOC signals.The proposed algorithm further enhances the dynamic adaptability and anti-noise performance and has superior performance in detection probability and acquisition time in comparison with other algorithms.     

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.     

LFM信号参数估计误差分析及改进算法

为进一步提高LFM信号参数估计的实时性和精确性,研究分析了分数阶傅里叶变换(FRFT)对LFM信号进行参数估计的误差来源,通过对其参数估计公式的研究,得出采样点数和搜索间隔对估计精度的不同影响,并提出了相应的改进算法。同时,采用DPT算法与FRFT离散并行快速算法相结合,提高了算法的实时性。最后,利用能量重心法对参数估计结果进行谱校正,进一步提高了估计精度。仿真结果表明,该算法对LFM信号参数估计精度高,性能接近CRLB,同时能够实现实时处理。     

FRFT与PTS联合降低OFDM系统峰均功率比的方法

针对OFDM系统的高PAPR,提出了将FRFT与PTS相结合的方法解决这一问题。首先利用FRFT代替FFT,使得系统的PAPR在适当的阶次下有了一定的下降。为更加有效地降低系统PAPR,将FRFT与PTS法相结合应用到OFDM中,并根据FRFT的酉性,简化了最优阶次的搜索算法,寻找出当系统阶次为0.006时其PAPR性能达到最优。仿真结果表明,当CCDF=10-4时,最优阶次下的OFDM系统将其PAPR降低了约7 dB,比使用传统PTS法时降低了约4 dB。     

基于分数阶傅里叶变换的LFM信号参数估计预判法

本文提出了一种基于分数阶傅里叶变换(fractional Fourier transform,FRFT)的LFM信号参数预判法,即对线性调频信号先利用FFT进行预判并粗略估计出调频系数值;根据得到的线性调频系数计算出对应的旋转角度,然后根据这个旋转角度做FRFT以找出精确的最大峰值点并精确估计出信号的各参数.理论分析与仿真结果表明该方法在保留原FRFT优点的同时,极大地减少了计算量,为单分量LFM信号的实时处理提供了可能;同时也避免了原基于FRFT(fractional Fourier transform)的二维搜索所带来的频谱伪峰干扰.     

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     

两种海杂波背景下的微弱匀加速运动目标检测方法

该文研究了线性调频(LFM)信号和单频信号分数阶Fourier变换(FRFT)模函数的一些性质,根据这些性质提出了两种基于FRFT模之差的海杂波背景下匀加速运动目标检测的新方法。一种方法利用接收信号与其延时信号的FRFT模之差,另一种方法利用接收信号正旋转角的FRFT模与负对称旋转角FRFT模的镜像之差。两种方法能较有效地抑制海杂波,对信杂比有一定的改善,在低信杂比下具有较好的检测效果。对实测海杂波数据进行仿真,证实了两种方法的有效性。     

The fractional Fourier transform and time-frequency representations

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 α and can be interpreted as a rotation by an angle α in the time-frequency plane. An FRFT with α=π/2 corresponds to the classical Fourier transform, and an FRFT with α=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