多项式相位信号参数估计新方法*

改进了多项式相位变换,并提出了多相位信号参数估计方法。仿真证明了在较低信噪比的情况下,参数估计的均方误差接近Cramer-Rao界。采用傅里叶系数插值的频率估计算法提高了该方法的运算速度。分析和仿真结果证明了该方法的有效性。     

一种高阶QAM信号多普勒频率参数估计算法

为了解决高阶QAM信号载波同步中动态多普勒参数估计问题,提出一种基于调频信号相位多项式系数估计的方法。该方法针对QAM信号去调制后的信号表达式构造二次相位(QP)函数,通过估计二次项系数得到多普勒频率变化率的估计值。进一步依据最大似然准则估计一次项相位系数得到多普勒频率估计值。最后用取主值的方法进行相位估计。计算机仿真实验表明,该算法能有效应用于高阶QAM信号的动态多普勒参数估计。     

基于DPT的非线性调频信号的DOA估计

提出了一种基于DPT的宽带非线性调频信号的DOA估计算法.首先将非线性调频(NLFM)信号建模为高阶多项式相位信号(PPS)模型,然后通过高阶瞬时矩进行多项式相位变换.接收信号将变换为单个正弦信号和新的噪声.再利用ROOT-MUSIC或者ESPRIT算法对变换得到的正弦信号的波达方向进行估计.理论分析和仿真结果表明,该...     

基于相位相关的小波域图像配准方法研究

小波变换是一种多尺度信号分析方法,近几年在图像处理领域受到广泛关注,它克服了傅立叶变换的固定分辨率的弱点,既可分析信号概貌,又可分析信号的细节。相位相关是一种频率域的图像配准参数估计方法,是利用傅立叶变换的平移、旋转等特性进行参数估计的。在研究多尺度小波分析和相位相关理论的基础上,提出基于小波系数的像素级相位相关图像配准方法：首先对待配准图像进行小波分解,获得低频小波系数后,再对小波系数应用相位相关进行配准参数估计。实验结果表明了该方法的可行性和有效性。     

基于小波变换的相位编码调制函数估计方法

相位编码调制信号是一种典型的脉冲压缩信号.广泛应用于脉冲压缩体制的低截获概率雷达中.获取相位编码信号的编码规律对单站无源定位与跟踪十分重要.推导了基于连续小渡变换的相位编码信号相位调制函数的估计算法,能得到相位编码信号的相位调制函数的正确估计.计算机仿真结果证明了算法的有效性.     

一种高精度声表面波标签无线测温算法

针对声表面波(Surface Acoustic Wave,SAW)标签无线测温时延估计精度不高的问题,提出了一种新算法;为降低对硬件转换速度和采样率的要求,采用频率步进连续波(Frequency stepped continuous wave,FSCW)作标签的询问信号,利用频率粗估计和相位精估计相结合的方法提取回波时延;推导出回波时延估计的Cramer-Rao下限(CRLB),分析了相关参数对其影响;在复加性高斯白噪声条件下,利用时延估计结果进行无线测温;当起始频率为902MHz,扫描带宽为26MHz时,仿真结果证明该算法测温误差仅为频率估计算法的1/60。     

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。     

低信噪比下的脉内调制方式识别

雷达信号脉内调制方式识别是电子对抗的重要内容。在实信号进行正交变换的过程中通过频域滤波来滤除带外噪声,提高信噪比,对滤波后的复信号利用改进的相位展开算法计算瞬时相位,然后对得到的瞬时相位曲线进行三次多项式拟合,根据拟合离差、拟合参数来自动识别常规信号、线性调频信号和三次多项式相位信号,并在正确识别调制方式的基础上进行参数估计。计算机仿真表明,本方法在信噪比不小于0dB的条件下,正确识别率达90%以上,且参数估计的精度较高。     

基于复移Morlet小波的光栅条纹图像相位提取

面结构光三维测量中,变形光栅条纹的相位信息映射为物体表面的高度,为了提取变形条纹的相位,用复移Morlet小波作为母小波,并给出复移Morlet小波的参数带宽fb和中心频率fc的取值方法,对由物体表面高度调制而发生变形的条纹图像进行连续小波变换,利用最大值法确定小波变换的"脊",进而提取条纹图像的相位.模拟和实验均表明...     

基于小波变换的载波相位恢复算法的研究

在相干光通信中,利用载波相位恢复算法来减小由于激光器线宽和ASE噪声所造成的相位偏移。对载波相位恢复算法进行了研究,在此基础上提出了利用小波变换中的小波分解和小波重构的方法来减少相位噪声,通过结果比对选取最佳分解次数,并对此方法进行迭代,以提高去噪性能。小波分析中用到的小波系数没有唯一性,仿真中选取了不同的基小波并对结果进行分析。结果表明通过小波变换算法能够有效地对激光器的初始相位偏差进行恢复,为载波相位恢复提供了一种新的解决办法。     

基于数据辅助的CPM信号捕获与频偏估计算法

针对连续相位调制（Continuous phase modulation,CPM）信号同步问题,提出了一种基于给定导频符号的信号捕获和频偏估计算法。首先对由扩频码字组成的导频信号进行复数匹配滤波,将输出值取模,通过搜索峰值实现信号捕获。然后根据相关值对应的辅角利用最小二乘法实现频偏和初相估计,并分析推导了该算法在较低信噪比条件下的频偏估计范围和估计精度。仿真实验结果表明,频偏估计范围可达±0.25倍的符号速率,与传统伪码相关估计频偏算法相比,该算法估计精度高,在SNR为3.3 dB时频偏估计精度接近克拉美罗下界。     

全相位FFT测相方差及其Cramer- Rao下限

为揭示全相位FFT(All-phase FFT,apFFT)相位测量法相比于现有方法精度提高的内在原因,在前人基础上,结合谱泄漏分布特征及其参数估计理论,推导出了适用于apFFT测相的两未知参数估计模型的克拉罗下限(Cramer-Rao lower bound,CRLB),以及apFFT测相误差与频偏分布的关系.指出该理论下限比传统三参数的CRLB低12 dB,基于此总结出一系列在不同信噪比和不同频偏情况下的apFFT测相性能结论.仿真结果表明:在不同的信噪比环境和不同的频偏情况下,apFFT的测相方差都能被CRLB2所界定,验证了上述结论及其新的CRLB正确性.     

Bounds on the accuracy of Gaussian ARMA parameter estimation methods based on sample covariances

The asymptotic accuracy of Gaussian ARMA parameter estimation methods based on a fixed number of sample covariances is considered. Several key results are briefly reviewed, including: i) a general asymptotic expression for the error covariance of the ARMA parameter estimates; ii) the fact that this error covariance is always greater than a certain lower bound; iii) the fact that this lower bound is strictly greater than the Cramer-Rao bound; iv) an explicit ARMA estimation technique that asymptotically achieves the bound. The key result of this note is a proof that this lower bound approaches the Cramer-Rao bound as the number of sample covariances tends to infinity.     

基于奇异值分解的方向估计改进方法

基于相位匹配原理的奇异值分解法(Singular value decomposition based on signal phase matching,SVDSPM)的波达方向估计的均方根误差在高信噪比下无法逼近克拉美罗界,针对该问题提出了基于相位匹配原理的修正奇异值分解法(Modified singular value decomposition based on signal phase matching,MSVDSPM)。该方法将阵列接收信号转换到频域,取相位匹配后各阵元中心频点频谱与其均值差值的距离平方和的倒数作为方向估计算子。仿真表明MSVDSPM方向估计的均方根误差可以在高信噪比下逼近克拉美罗界。MSVDSPM保持了SVDSPM在单源入射时的尖锐谱峰,它等价于常规波束形成方法,并且其主瓣宽度与分析频率无关。     

WSN基于声音能量量化数据的目标定位研究

无线传感器网络的通信带宽和能量是有限的,只传输二进制或几个比特量化数据来完成目标定位任务可以减少网络开销。对无线传感器网络目标定位方法进行了研究,采用声音能量衰减模型,推导了基于量化信号的似然函数,给出了基于声音能量量化数据的最大似然定位方法。为了对估计结果进行评定,推导了最大似然估计的克拉美罗下限。仿真结果表明,该方法对目标定位的准确度基本接近于克拉美罗下限。因此,在满足定位精度的前提下,可通过减少传递的比特位数来节约网络的能量。     

Accuracy Analysis of the Frisch Scheme for Identifying Errors-in-Variables Systems

Several estimation methods have been proposed for identifying errors-in-variables systems, where both input and output measurements are corrupted by noise. One of the promising approaches is the so-called Frisch scheme. This paper provides an accuracy analysis of the Frisch scheme applied to system identification. The estimates of the system parameters and the noise variances are shown to be asymptotically Gaussian distributed. An explicit expression for the covariance matrix of the asymptotic distribution is given as well. Numerical simulations support the theoretical results. A comparison with the Cramer-Rao lower bound is also given in the examples, and it is shown that the Frisch scheme gives a performance close to the Cramer-Rao bound for large signal-to-noise ratios (SNRs).     

On the tightness of some error bounds for the nonlinear filtering problem

A comparison between the exact filtering error and lower bounds on that error is made using Benes' solution to the nonlinear filtering problem. It is shown that the Bobrovsky-Zakai bound, based upon a version of Cramer-Rao bound, is tight over a range of signal-to-noise ratios of the observation process.     

On achievable accuracy in edge localization

Edge localization occurs when an edge detector determines the location of an edge in an image. The authors use statistical parameter estimation techniques to derive bounds on achievable accuracy in edge localization. These bounds, known as the Cramer-Rao bounds, reveal the effect on localization of factors such as signal-to-noise ratio (SNR), extent of edge observed, scale of smoothing filter, and a priori uncertainty about edge intensity. By using continuous values for both image coordinates and intensity, the authors focus on the effect of these factors prior to sampling and quantization. They also analyze the Canny algorithm and show that for high SNR, its mean squared error is only a factor of two higher than the lower limit established by the Cramer-Rao bound. Although this is very good, the authors show that for high SNR, the maximum-likelihood estimator, which is also derived, virtually achieves the lower bound     

Optimality analysis of sensor-target localization geometries

The problem of target localization involves estimating the position of a target from multiple noisy sensor measurements. It is well known that the relative sensor-target geometry can significantly affect the performance of any particular localization algorithm. The localization performance can be explicitly characterized by certain measures, for example, by the Cramer-Rao lower bound (which is equal to the inverse Fisher information matrix) on the estimator variance. In addition, the Cramer-Rao lower bound is commonly used to generate a so-called uncertainty ellipse which characterizes the spatial variance distribution of an efficient estimate, i.e. an estimate which achieves the lower bound. The aim of this work is to identify those relative sensor-target geometries which result in a measure of the uncertainty ellipse being minimized. Deeming such sensor-target geometries to be optimal with respect to the chosen measure, the optimal sensor-target geometries for range-only, time-of-arrival-based and bearing-only localization are identified and studied in this work. The optimal geometries for an arbitrary number of sensors are identified and it is shown that an optimal sensor-target configuration is not, in general, unique. The importance of understanding the influence of the sensor-target geometry on the potential localization performance is highlighted via formal analytical results and a number of illustrative examples.