地震动统一时频谱特性及其在人工合成中的应用

传统地震动时域或频域理论模型不能充分反映地震动时频域的非平稳性。利用现代信号分析方法可以获得非平稳时变功率谱及演变谱,但缺乏精度评价标准。在理论和计算分析的基础上,确认小波包分解方法是建立时变功率谱的有效工具,并依据时频边缘条件提出时频谱分析精度评价标准。在地震动时频特征统计分析的基础上,认为地震动时频谱由多峰平滑谱和非平稳噪声谱两部分构成。分别建立由多峰平滑功率谱和非高斯频域噪声谱构成的地震动频域谱模型以及由多峰平滑时域能量谱模型和非高斯时域噪声谱模型构成的地震动时域谱模型。由此形成的时频谱模型在时频特性表征、函数表达、精度及多维分析等方面均有较强的统一性。依据统一时频谱进行调整或人工合成的地震动具有良好的精度和强非平稳性。     

微弱随机信号功率谱的互相关测量原理及性能研究

本文提出一种采用双通道前放的互功率谱估计方法。对互谱周期图估计的偏量及方差计算表明,互谱估计的期望值可以消除前放噪声及零漂等影响,从而保证了微弱信号功率谱的准确测量。文中指出,前放噪声会加大互谱估计的方差,并引起幅值估计的附加偏量,因此采用低噪声前放及平滑周期图平均法仍是十分必要的。用本方法对低于前放噪声一个数量级的小电阻热噪声及二极管PN结电流散弹噪声功率谱进行了测量,测量结果与理论值基本一致。     

多轴平稳非高斯随机振动试验控制方法

多轴向平稳非高斯随机振动控制试验能够对指定响应信号的时、频域特征进行同时控制。提出了一种快速生成具有指定功率谱密度、斜度和峭度的平稳非高斯随机振动信号的方法。通过频率采样方法将目标功率谱密度设计成滤波器,利用非线性变换方法获取非高斯随机信号,再将此信号经过设计的滤波器以获得满足要求的非高斯随机信号。该方法简单、快速并克服了传统非线性变换方法的缺点。进一步将此方法应用于三轴向平稳非高斯随机振动试验中,给出了三轴向非高斯随机振动控制的闭环均衡步骤,此方法能够同时对信号的功率谱自谱、相干系数、相位差以及斜度和峭度进行解耦控制。进行了三轴向平稳非高斯随机振动控制试验,三个方向上加速度信号的功率谱密度、斜度和峭度控制效果均令人满意,满足工程应用要求。     

一种尺度自适应Canny边缘检测方法

作为最优边缘检测算子,Canny算子在线性滤波及选取阈值方面具有一定的局限性.在其基础上,采用尺度可变的自适应高斯滤波器进行滤波,通过计算滤波器各滤波窗口内像素点灰度值的均值,以及该均值与当前像素点灰度值的差值,确定滤波窗口的平滑度,并将其作为高斯函数尺度的大小,在滤除噪声的同时确保了边缘信号不被平滑掉;同时采用基于简单统计法及梯度阚值法的双阈值分割方法检测经过非极大值抑制的梯度幅值图像的边缘.实验结果表明,该方法能够有效滤除噪声,且假边缘及丢失边缘的数量都比Canny边缘检测的少.     

非高斯噪声中随机信号的渐近最优检测

本文研究了非高斯噪声中随机信号的检测问题。基于随机信号的参数模型和广义似然比检测理论,导出了非高斯噪声中随机信号Rao检测的数学解析式,其检测性能渐近等同于广义似然比检测但计算更有效。仿真结果表明,该检测器性能大大优于传统的能量检测器以及高斯噪声假设下的广义似然比检测器。     

用互谱法检测噪声中周期信号的周期和功率谱

本文提出了一种从噪声中检测周期信号的周期和功率谱的新方法,即使用互谱进行同步相加的方法,并从理论和实验上进行了研究。具体地说,该方法是在保持一定时间延迟的条件下,从掩埋在噪声中的周期信号上进行两次取样,计算两次取样之间的互谱,再对互谱进行多次同步相加。本方法与以前使用触发脉冲的功率谱法不同,它的一个特点是不需要触发脉冲。根据理论分析可知,用上述方法得到的互谱就等于周期信号的功率谱被旋转了一个同时间差和频率的积成比例的相位。本文还分别以掩埋在白噪声中的二个正弦波和由掩埋在白噪声中的短周期M序列信号构成的周期噪声信号为对象进行了实验验证。结果表明,本方法对高精度地检测噪声中的周期信号的周期和功率谱是有效的。     

多维自回归(AR)模型计算地震动空间相干函数的适用性研究

传统周期图法求解地震动相干函数时需要进行功率谱的人为加窗平滑,且仅能考虑记录两两之间的空间相关性.针对以上缺陷,该研究采用多维自回归(autoregressive,AR)模型代替周期图法进行相干函数计算,并从多个方面对其合理性加以评估和验证.首先以中国台湾SMART-1台阵数据为算例,对比AR模型以及周期图法计算相干函...     

基于滤波器组和高阶累积量的信号特征检测

结合高阶累积具有对加性高斯和对称非高斯噪声不敏感的特性，提出了一种基于滤波器组和高阶累积量的特征检测方法。该方法既不需待检信号做高斯和平稳的假设，也不需要有信号的先验知识。其原理是首先通过滤波器组将检测信号在频域上进行分离，选取输出能量较大的一组子频带信号近似给出信号的时频描述；然后在各个选中的子频带内分别计算三阶累积量的短时估计，从而抑制有色噪声，将信号的特征检测出来。仿真信号和实验信号验证了该方法的有效性和适应性，即使信号特征完全淹没在噪声中，也能检测出来。     

一种检测低信噪比响应信号的广义维纳滤波方法

根据所测动态响应信号拟合系统模型时,一个重要的问题是:在低信噪比的情况下如何提高系统参数的估计精度。采用线性平均方法能适当提高信噪比,但在一般试验条件下,据此所获得的部分系统参数仍具有较大的误差。本文采用了时域广义维纳滤波方法对测取的动态信号进行预处理,以尽可能少的激励次数,从较高的背底噪声中检测出真实信号。文中还说明了适用于工程系统测试中噪声功率估计的滤波器分组方法。以悬臂梁的模态测试及分析为例,检验了该方法的有效性。试验表明,作振型分析时,因测点信号信噪比低而导致数据误差大的问题得以解决,这对实验模态分析是具有重要意义的。所完成的计算机-FFT 谱分析器信号预处理程序有很大的实用价值。     

舰艇辐射噪声特性仿真

本文讨论了舰艇辐射噪声的特性.运用冲激响应不变法来设计了一个IIR数字一阶低通滤波器,并用此滤波器对高斯白噪声进行滤波,滤波器的输出能很好地模拟舰艇辐射宽带连续噪声,此输出信号的功率谱在高频端以每倍频程6分贝的斜率下降.运用直接数字信号合成技术产生多个低频线谱信号,并将它与宽带连续噪声相加,从而来模拟舰艇辐射的低频线谱.     

A New Method for Rolling Element Bearing Fault Diagnosis Based on Cyclostationary Theory

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A Reappraisal of the Period Spectral Analysis

The purpose of this paper is to revive interest in the periodogram approach to time series analysis which, at present, is only of historical interest and is seldom used. During the late 1940's, when it was realized that the smoothed periodogram could be used to estimate the spectral density of a stationary time series, the method was impractical because of the amount of computations. This is no longer the case, but not realized by many applied workers. In this paper the smoothed periodogram is considered from the point of view of its spectral window. The results are compared with two standard spectral windows by a method which avoids defining a bandwidth. The spectral windows are normalized so that they have the same variance, and plotted. The user can then choose the window which best suits his needs. Rejection filtering, trigonometric regression and cross-spectra analysis are discussed. An example is given in which the spectra and cross-spectrum of a bivariate time series are estimated.     

Coherent Doppler lidar signal spectrum with wind turbulence

The average signal spectrum (periodogram) for coherent Doppler lidar is calculated for a turbulent wind field. Simple approximations are compared with the exact calculation. The effects of random errors in the zero velocity reference, the effects of averaging spectral estimates by use of multiple lidar pulses, and the effects of the range dependence of the lidar signal power over the range gate are included. For high spatial resolution measurements the lidar signal power is concentrated around one spectral estimate (spectral bin), and correct interpretation of the contribution from turbulence is difficult because of the effects of spectral leakage. For range gates that are larger than the lidar pulse volume, the signal power is contained in many spectral bins and the effects of turbulence can be determined accurately for constant signal power over the range gate and for the far-field range dependence of the signal power.     

Adaptive spectral doppler estimation

In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence. The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal. Adaptive spectral estimation techniques are known to provide good spectral resolution and contrast even when the observation window is very short. The 2 adaptive techniques are tested and compared with the averaged periodogram (Welch's method). The blood power spectral capon (BPC) method is based on a standard minimum variance technique adapted to account for both averaging over slow-time and depth. The blood amplitude and phase estimation technique (BAPES) is based on finding a set of matched filters (one for each velocity component of interest) and filtering the blood process over slow-time and averaging over depth to find the PSD. The methods are tested using various experiments and simulations. First, controlled flow-rig experiments with steady laminar flow are carried out. Simulations in Field II for pulsating flow resembling the femoral artery are also analyzed. The simulations are followed by in vivo measurement on the common carotid artery. In all simulations and experiments it was concluded that the adaptive methods display superior performance for short observation windows compared with the averaged periodogram. Computational costs and implementation details are also discussed.     

Facts and fiction in spectral analysis

This analysis is limited to the spectral analysis of stationary stochastic processes with unknown spectral density. The main spectral estimation methods are: parametric with time series models, or nonparametric with a windowed periodogram. A single time series model will be chosen with a statistical criterion from three previously estimated and selected models: the best autoregressive (AR) model, the best moving average (MA) model, and the best combined ARMA model. The accuracy of the spectrum, computed from this single selected time series model, is compared with the accuracy of some windowed periodogram estimates. The time series model generally gives a spectrum that is better than the best possible windowed periodogram. It is a fact that a single good time series model can be selected automatically for statistical data with unknown spectral density. It is fiction that objective choices between windowed periodograms can be made     

Broadband radio-frequency spectrum analysis in spectral-hole-burning media

We demonstrate a 20 GHz spectrum analyzer with 1 MHz resolution and >40 dB dynamic range using spectral-hole-burning (SHB) crystals, which are cryogenically cooled crystal hosts lightly doped with rare-earth ions. We modulate a rf signal onto an optical carrier using an electro-optic intensity modulator to produce a signal beam modulated with upper and lower rf sidebands. Illuminating SHB crystals with modulated beams excites only those ions resonant with corresponding modulation frequencies, leaving holes in the crystal's absorption profile that mimic the modulation power spectrum and persist for up to 10 ms. We determine the spectral hole locations by probing the crystal with a chirped laser and detecting the transmitted intensity. The transmitted intensity is a blurred-out copy of the power spectrum of the original illumination as mapped into a time-varying signal. Scaling the time series associated with the transmitted intensity by the instantaneous chirp rate yields the modulated beam's rf power spectrum. The homogeneous linewidth of the rare-earth ions, which can be <100 kHz at cryogenic temperatures, limits the fundamental spectral resolution, while the medium's inhomogeneous linewidth, which can be >20 GHz, determines the spectral bandwidth.     

PSD和PWELCH函数的分析改进及应用

针对MATLAB中两个内建功率谱密度计算函数psd()和pwelch()计算结果迥异的现象,在功率谱密度估计理论的基础上,根据经典的周期图理论和Welch平均周期图方法,通过详细分析源程序,解析计算方法,发现psd()计算的并不是工程单边功率谱密度,而是采样信号双边谱,故与pwelch()结果迥异,另外pwelch()不能对分段信号数据进行预处理。就上述不足提出了相应的改进措施,比较验证表明改进措施行之有效。     

Simulation of stationary Gaussian/non-Gaussian stochastic processes based on stochastic harmonic functions

A new model is proposed to represent and simulate Gaussian/non-Gaussian stochastic processes. In the proposed model, stochastic harmonic function (SHF) is extended to represent multivariate Gaussian process firstly. Compared with the conventional spectral representation method (SRM), the SHF based model requires much fewer variables and Cholesky decompositions. Then, SHF based model is further extended to univariate/multivariate non-Gaussian stochastic process simulation. The target non-Gaussian process can be obtained from the corresponding underlying Gaussian processes by memoryless nonlinear transformation. For arbitrarily given marginal probability distribution function (PDF), the covariance function of the underlying multivariate Gaussian process can be determined easily by introducing the Mehler’s formula. And when the incompatibility between the target non-Gaussian power spectral density (PSD) or PSD matrix and marginal PDF exists, the calibration of the target non-Gaussian spectrum will be required. Hence, the proposed model can be regarded as SRM to efficiently generate Gaussian/non-Gaussian processes. Finally, several numerical examples are addressed to show the effectiveness of the proposed method.     

非ARMA随机信号谱估计的数字滤波算法及性能分析

为克服传统的周期图谱估计方法存在的分辨率低及非一致估计等缺点而迅速发展的各种近代谱估计法只能适用于ＡＲＭＡ（Ａｕｔｏｒｅｇｒｅｓｓｉｖｅ－ｍｏｖｉｎｇａｖｅｒａｇｅ）信号的谱估计。为解决存在于各种物理过程中的非ＡＲＭＡ随机过程（包括１／ｆ过程）的谱估计，本文提出了一种对非ＡＲＭＡ随机信号的非递归数字滤波算法，重点研究了算法主要参数（滤波器长度、数据长度）的选择原则及对谱估计准确度和精度的影响。理论分析及实际测量证明，此方法具有分辨率高、谱估计精确较好及一致估计等优点，特别适用于对非ＡＲＭＡ信号的某些特征频率点的谱估计。     

Reliable LDA-spectra by resampling and ARMA-modeling

Laser-Doppler Anemometry (LDA) is used to measure the velocity of gases and liquids with observations irregularly spaced in time. Equidistant resampling turns out to be better than slotting techniques. After resampling, two ways of spectral estimation are compared. The first estimate is a windowed periodogram and the second is the spectrum of a time series model. That is an estimated autoregressive moving average (ARMA) process whose orders are automatically selected from the data with an objective statistical criterion. Typically, the ARMA spectrum is better than the best windowed periodogram