改进的DFT正弦信号频率估计

通过分析Candan算法和2N点DFT算法的性能,本文提出了一种改进的基于DFT的正弦信号频率估计算法。在对原始信号进行必要的离散化预处理后,在粗估计阶段利用Candan算法估计出频率偏差后,利用该频偏对原始信号进行频率修正。然后对修正后的原始信号进行2N点DFT算法精估计。由于增加了原始信号的频率修正步骤,该算法发挥了Candan算法和2N点DFT算法的优点,同时避免了其缺点。仿真结果表明,在相对频偏为任意值时,改进算法频率估计的均方根误差均接近克拉美罗下限,并且估计性能优于现有的频率估计算法。      

一种双正弦信号的快速频率测量方法

信号频率测量在雷达信号处理中起到重要的作用。基于DFT和自相关理论,该文提出了一种双正弦信号频率的快速估计方法。该方法先用DFT估计其中一个频率及其幅度,以此频率对信号解调并对消该频率成分,最后利用自相关理论估计信号的频差。计算机模拟证实了方法具有精度高、测频速率快的特点。     

基于插值DFT的正弦信号高精度参数估计方法

采用传统的DFT对正弦信号进行参数估计，估计精度会因频谱泄漏而降低。本文分析了基于插值DFT的对单分量正弦信号参数的精确估计方法。仿真试验表明，采用该方法估计单分量正弦信号参数时为近似无偏估计，估计误差方差接近Crammer-Rao界。在此基础上本文提出了对多分量正弦信号参数进行精确估计的插值DFT方法，该方法能很好地抑制噪声和分量信号间的旁瓣干扰，当各分量信号间不发生主瓣重叠时为近似无偏估计，估计误差方差接近Crammer-Rao界。     

噪声背景下一种正弦信号频率估计的新插值算法

本文研究噪声中正弦信号频率的估计问题.结合DFT变换和插值方法,提出了一种基于DFT变换系数的频率估计算法,其计算简单.结果表明,该方法有效地改善了频率估计精度,且便于工程实现.     

利用DFT和迭代校正的正弦信号频率估计算法

对电磁信号的频率估计广泛应用于通信、雷达、导航和电子对抗等领域。针对正弦信号的频率精确估计,本文提出了一种利用DFT和迭代校正的频率估计算法,并与现有三种基于DFT的频率估计算法进行了性能仿真对比,分析结果表明,新算法的频率估计性能明显优于其他三种算法,可以得到非常逼近CRLB的频率估计值,迭代校正5次时,频率估计的RMSE距离CRLB不到0.07dB,而且没有估计误差平层,算法估计精度高,对频率的取值范围不敏感,性能稳定,迭代校正的复杂度较低,具有很好的应用价值。      

基于立方卷积插值的信号DFT频谱校正算法

由于栅栏效应的存在,会使得基于信号DFT谱峰搜索的测频算法产生很大误差,因此在测频之前一般需对信号的DFT频谱进行校正。推导出用于单正弦信号DFT频谱校正的立方卷积插值核函数、插值函数及形状参数,并依据脉冲雷达信号是无数个正弦信号之和的事实,将其推广至脉冲雷达信号DFT频谱的幅度校正,给出其频谱检测算法。     

一种基于频率校正的BPSK信号高精度载频估计算法

为了实现BPSK信号载频的高精度估计,提出一种基于频率校正的高精度BPSK信号载频估计算法。算法通过非线性变换将BPSK信号的载频估计问题转化为对点频信号的估计,再用DFT对点频信号进行粗估计,针对DFT估计频率分辨率差的问题,采用相位差校正法对DFT估计频率进行校正,从而实现BPSK信号载频的高精度估计。最后,通过性能分析和计算机仿真验证了算法的有效性,其结果表明:该算法在低信噪比情况下能获得较好的估计精度,在高信噪比情况下,估计精度大幅提高。     

一种改进的CPFSK信号频率估计方法

针对突发信号解调中多普勒频偏大的问题,提出了一种改进的连续相位移频键控信号(CPFSK)频率估计方法.首先接收信号平方运算使信号的调制指数加倍,再通过一次离散傅里叶变换(DFT),用搜索信号平方谱谱峰的方法实现突发信号频率估计.仿真实验表明,在低信噪比条件下,该算法的频率估计精度比经典的相位差频率估计算法提高了20%;该算法与广泛用于高动态突发信号的频率插值估计算法相比,同道干扰信道下信干比改善可达4 dB,DFT长度缩短约1/3.     

DFT相位估计算法及噪声敏感频率问题分析

该文利用参数估计方差最小为优化准则,计算出多频正弦信号分段WDFT(Windowed-DFT)相位加权平均相位估计算法的最优加权系数,并给出算法相位估计的方差公式。另外,该文对DFT噪声敏感频率区域问题进行了详细的理论分析。计算机仿真证明了算法的有效性和理论分析的正确性。     

单频信号快速频率估计算法比较及改进

本文首先讨论并分析了各种插值DFT(离散傅里叶变换)算法,然后提出了一种实正弦信号的快速插值频率估计方法。该方法只需 3个DFT变换系数的实部构造频率修正项,计算量低,具有精度高、测频速率快的特点。计算机模拟和FPGA仿真均证实了该方法的有效性。     

Coarse frequency estimation using the discrete Fourier transform (Corresp.)

The discrete Fourier transform (DFT) is applied as a coarse estimator of the frequency of a sine wave in Gaussian noise. Probability of anomaly and the variance of the estimation error are determined by computer simulation for several DFT block sizes as a function of signal energy-to-noise density ratiomathcal{E}/N_0. Several data windows are considered, but uniform weighting gives the best performance.     

A new algorithm for the estimation of the frequency of a complex exponential in additive Gaussian noise

The letter presents a new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise. The discrete Fourier transform (DFT)-based algorithm performs a frequency interpolation on the results of an N point complex fast Fourier transform. For large N and large signal to noise ratio, the frequency estimation error variance obtained is 0.063 dB above the Cramer-Rao bound. The algorithm has low computational complexity and is well suited for real time digital signal processing applications, including communications, radar and sonar.     

基于多通道的跳频信号欠采样频率估计

为实现跳频信号频率跟踪估计,本文提出一种基于多通道的跳频信号欠采样频率估计方法。基于快速傅里叶变换（FFT）,提出了一种3谱线方程的频率校正算法,提高了基于中国余数定理的频率估计方法对短序列信号的频率估计精度,与现有的两种基于离散傅里叶变换（DFT）的频率校正算法相比,序列补零数量灵活。给出了一种频率估计检错机制,可以提高算法可靠性。仿真结果表明,本文所提频率估计算法的精度优于现有算法,增加序列补零数量可进一步提高算法的估计精度和信噪比阈值,降低误差平台;检错机制在-23 dB至8 dB信噪比范围内的准确率高于95.5%。      

基于离散傅里叶变换的高动态突发信号检测及频率估计

针对高动态突发通信应用环境,提出了一种新的基于频率域的突发信号检测及载波频偏估计算法,通过一次离散傅里叶变换( DFT)实现突发信号存在性检测及频率估计,并与经典Power-Law算法进行了比较。仿真结果表明：在低信噪比条件下,新算法检测信噪比门限改善超过1 dB,频率估计均方根误差小于符号率的1‰,并且对载波频偏及信号电平动态不敏感,实现结构简单,适合实时处理及工程应用。     

从DFT导出过程和频谱分析谈DFT教学

信号处理中离散傅里叶变换DFT是一个重要的计算手段,可以完成很多计算,包括对连续时间信号的频谱估计。这在教学中是一个重点和难点。本文构建了由连续时间傅里叶变换CTFT和离散时间傅里叶变换DTFT导出DFT的过程,通过一系列操作和推导,以此理解DFT和DTFT以及离散时间傅里叶级数DTFS的密切联系,并深刻体会利用DFT做频谱分析的特点和考虑。     

Algebraic Signal Processing Theory: Cooley–Tukey Type Algorithms for Real DFTs

In this paper, we systematically derive a large class of fast general-radix algorithms for various types of real discrete Fourier transforms (real DFTs) including the discrete Hartley transform (DHT) based on the algebraic signal processing theory. This means that instead of manipulating the transform definition, we derive algorithms by manipulating the polynomial algebras underlying the transforms using one general method. The same method yields the well-known Cooley-Tukey fast Fourier transform (FFT) as well as general radix discrete cosine and sine transform algorithms. The algebraic approach makes the derivation concise, unifies and classifies many existing algorithms, yields new variants, enables structural optimization, and naturally produces a human-readable structural algorithm representation based on the Kronecker product formalism. We show, for the first time, that the general-radix Cooley-Tukey and the lesser known Bruun algorithms are instances of the same generic algorithm. Further, we show that this generic algorithm can be instantiated for all four types of the real DFT and the DHT.     

The quick Fourier transform: an FFT based on symmetries

This paper looks at an approach that uses symmetric properties of the basis function to remove redundancies in the calculation of the discrete Fourier transform (DFT). We develop an algorithm called the quick Fourier transform (QFT) that reduces the number of floating-point operations necessary to compute the DFT by a factor of two or four over direct methods or Goertzel's method for prime lengths. By further application of the idea to the calculation of a DFT of length-2^M , we construct a new O(NlogN) algorithm, with computational complexities comparable to the Cooley-Tukey algorithm. We show that the power-of-two QFT can be implemented in terms of discrete sine and cosine transforms. The algorithm can be easily modified to compute the DFT with only a subset of either input or output points and reduces by nearly half the number of operations when the data are real     

Some detection and resolution properties of maximum entropy spectrum analysis

An experimental comparison between conventional spectral estimation techniques and a Maximum Entropy Spectral Analysis (MESA) algorithm is made. Three factors in the experimentation make the results of considerable interest to workers in acoustic signal processing, especially sonar and surveillance. These are the range of signal-to-noise ratio (SNR) studied, the comparisons based equal length observation intervals and the use of ensemble averaging after maximum entropy analysis. Results are presented, for both resolution and peak signal response, which tend to indicate that the Maximum Entropy Method (MEM) offers considerable promise in achieving the detection performance of long observation interval discrete Fourier transform (DFT) analysis at a much reduced length of observation time.     

离散傅里叶变换的算术傅里叶变换算法

离散傅里叶变换(DFT)在数字信号处理等许多领域中起着重要作用.本文采用一种新的傅里叶分析技术—算术傅里叶变换(AFT)来计算DFT.这种算法的乘法计算量仅为O(N);算法的计算过程简单,公式一致,克服了任意长度DFT传统快速算法(FFT)程序复杂、子进程多等缺点;算法易于并行,尤其适合VLSI设计;对于含较大素因子,特别是素数长度的DFT,其速度比传统的FFT方法快;算法为任意长度DFT的快速计算开辟了新的思路和途径.