基于双树复小波的重叠块阈值降噪方法

针对机械早期故障信号受到强背景噪声影响导致故障特征不明显的问题,提出一种基于双树复小波包的重叠块阈值降噪方法。利用有限冗余双树复小波包变换对信号进行具有平移不变性的稀疏分解,结合双树复小波包变换系数所具有的稀疏集簇和邻域相关性特点,建立重叠块阈值估计模型,通过最小化包含块稀疏模型的适应度函数获得估计信号,分析了重叠块阈值降噪各参数对降噪效果的影响和参数优化原则,仿真与实测信号实验结果表明,该方法在不同信号和不同噪声水平下均有效地抑制了噪声干扰,提高了信噪比。     

光纤陀螺随机误差特性仿真与辨识

分析了光纤陀螺的各项随机误差特性,给出了各项随机噪声的仿真方法.运用不同的随机过程模拟各种随机噪声,采用正交小波变换的方法模拟1/f噪声,用白噪声一次离散积分的方法模拟速率随机游走噪声,用一阶马尔可夫过程模拟指数相关噪声,并给出了仿真结果.提出了基于Allan方差、小波分析和功率谱密度的随机误差辨识方法,对光纤陀螺的随机误差特性进行了辨识研究.     

基于sym8小波寻北系统去噪研究

针对寻北系统在实际运用中因受周围环境影响而导致寻北精度下降的问题,提出了基于小波变换的去噪方法.该方法选用sym8小波基,采用高次逼近法和改进的小波阈值对含有阵风噪声的原始陀螺信号进行小波去噪.结果表明sym8小波基为含阵风噪声陀螺信号进行小波去噪的最优小波基.调整其分解尺度进行仿真,发现分解尺度为4时达到了最佳去噪效果且提高了寻北精度,证明了方法的有效性与可靠性.     

基于粒子群优化的多小波图像降噪

提出一种基于粒子群优化的多小波图像降噪方法.该方法首先根据图像降噪的特点,采用粒子群算法优化CL多小波的前置滤波器,实现了图像多小波变换的自适应预滤波;接着对一幅含噪声图像进行多小波分解,根据多小波分解后的能量分布特性,对小波系数进行阈值处理;后经多小波反变换,得到重构图像.实验表明,本文方法的客观性能(PSNR)和主...     

基于小波包和LMS自适应降噪的柴油机振动诊断

由于柴油机振动信号的特征频带和噪声频带存在重叠现象,利用小波阈值消噪时难以选取合适的小波阈值,针对该问题提出一种基于小波包的LMS自适应滤波降噪方法。该方法将小波包与LMS自适应滤波相结合,首先利用小波包变换对信号进行多层分解,然后以噪声干扰对应尺度上的第一层“细节”分量及最大分解尺度上的逼近分量重构信号,将重构后的信号作为LMS自适应滤波器原始输入信号,再以小波包最大分解尺度上的高频细节信号作为自适应抵消器的参考输入信号,进行LMS自适应滤波降噪处理。仿真计算和工程应用表明,该方法参数设置较少,易于控制,不涉及小波阈值降噪中阈值的选取问题,对比试验信号的分析验证了方法的有效性,将该法应用在柴油机振动诊断中提高了故障识别率。     

基于偶树复小波和改进型阈值函数的降噪方法 及其应用

为更好地保留原有用信号信息,有效恢复强噪声背景下微弱故障信号,提出了一种基于对偶树复小波和改进型阈值函数的降噪方法,将其应用于机械故障诊断,取得了较好效果。运用对偶树复小波变换滤波器设计方法和改进型阈值函数,以实施降噪的具体步骤。该法充分利用了对偶树复小波变换的平移不变的优良特性,同时,改进型阈值函数与传统软、硬阈值降噪算法相比,克服了软阈值信号失真和硬阈值信号不连续、振荡等缺点。实验表明：此法有效去除了噪声,是一种较好的提取微弱故障信号的方法。     

几种超声NDE信号降噪方法的性能比较

为提高超声无损检测的准确性,需要对超声NDE信号中因随机分布于媒质中的大量散射微粒所引起的结构噪声进行降噪。由于信号和噪声的频谱范围基本重叠,传统的线性滤波方法不能提供理想的降噪结果。介绍了几种对超声NDE信号进行降噪的新方法:Wigner-Ville分布法、小波变换法和基于时间延迟的神经网络法,并从信噪比(SNR)、检测概率(PD)和估测深度(ED)等三个重要参数对它们的降噪性能进行计算机仿真实验的比较。结果表明:小波变换法和神经网络法的降噪效果较Wigner-Ville分布法要好。对实际信号的测试还表明,小波变换由于不像神经网络那样需要训练,是一种更为理想的超声NDE信号降噪方法。     

基于ATF与EEMD变转速齿轮故障特征降噪方法

针对变转速下的齿轮故障特征的降噪问题,提出了一种基于自适应时变滤波(Adaptive Time-varying Filtering, ATF)与集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)的齿轮故障特征降噪方法。该方法首先用线调频小波路径追踪(Chirplet Path Pursuit,CPP)算法从变转速下的齿轮故障振动信号中估计出齿轮啮合频率,并依据该啮合频率设计时变滤波器;再利用该时变滤波器对齿轮故障振动信号进行滤波,将滤波器阻带内的噪声予以去除;然后采用EEMD方法对滤波后的信号进一步降噪,减少滤波器通带内的噪声干扰;接着利用时变滤波器对降噪后的信号再次进行滤波,消除EEMD降噪时在阻带带来的噪声干扰;最后对降噪后的信号进行阶次分析,提取齿轮故障特征。对齿轮局部故障的算法仿真和应用实例分析表明,该方法不仅可以消除阻带的噪声干扰,而且对通带内的噪声也有较好的抑制作用,可有效凸显齿轮的故障特征。     

一种强噪声背景下的微弱信号检测的新方法

基于自适应滤波算法对小波滤波器的构造方法进行了研究,重点在于解决对强噪声环境下微弱信号提取所需要的小波变换的子带自适应滤波器的构造,把小波变换技术和自适应滤波技术应用于微弱信号的检测。提出基于自适应小波变换的心电信号的检测,利用小波变换的子带编码理论,通过在多个子带权值的自适应匹配,回复后拟合微弱信号。仿真结果表明,该方法可进一步改善信号的检测能力,在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

基于小波降噪算法的动态测试系统

针对目前陀螺导航装备缺乏动态性能测试方法的状况,本文提出并实现了车载动态性能测试系统.并根据航向数据非连续变化的特点,提出了基于周期平移的小波阈值降噪算法.该算法通过对信号的分段周期平移、阈值降噪、逆平移的方法实现降噪,有效地克服了常规小波阈值降噪算法带来的Pseudo-Gibbs现象.仿真及实测数据表明采用该算法能够在有效剔除异常点、消除噪声的同时,消除降噪信号中的Pseudo-Gibbs现象.跑车实验表明车载动态性能测试系统为陀螺导航设备提供了有效的解决方案和统一的测试平台.     

Estimations of frequency and its drift rate

This paper presents an analysis of frequency and its drift rate estimation by the difference method, the least-squares method, and the Kalman filter. Error formulas are derived for all five noise processes: white phase, flicker phase, white frequency, flicker frequency, and random walk frequency. The error formulas show the relationship between the estimate error and the noise spectral density coefficients, the same interval τ, and the data number N. Because of the existence of some nonstationary noise processes, a large data number may not yield a good estimation. One should choose an appropriate sample interval and data number so as to control the estimate error. An optimal solution based on the Kalman filter is presented     

Neural filtering of colored noise based on Kalman filter structure

In this paper, adaptive filtering approaches of colored noise based on the Kalman filter structure using neural networks are proposed, which need not extend the dimensions of the filter. The colored measurement noise is first modeled from a Gaussian white noise through a shaping filter. The Kalman filtering model of colored noise is then built by adopting an equivalent observation equation, which can avoid the dimension extension and complicated computations. An observation correlation-based algorithm is suggested to estimate the variance of the measurement noise by use of a single layer neural network. The Kalman gain can be obtained when a perfect knowledge of the plant model and noise variances is given. However, in some cases, the difficulties of the correlative method and the Kalman filter equations are the amount of computations and memory requirements. A neural estimator based on the Kalman filter structure is also analyzed as an alternative in this paper. The Kalman gain is replaced by a feedforward neural network whose weight adjustment permits minimization of the estimation error. The estimator has the capability of estimating the states of the plant in a stochastic environment without knowledge of noise statistics. If the noise of the plant is white and Gaussian and its statistics are well known, the neural estimator and the Kalman filter produce equally good results. The neural filtering approaches of colored noise based on the Kalman filter structure are applied to restore the cephalometric images of stomatology. Several experimental results demonstrate the feasibility and good performances of the approaches.     

多普勒计程仪的数据降噪算法

多普勒计程仪输出的船速数据中含有偏差较大的点,即野点,在数据处理时必须将其去除,否则可能会导致组合导航系统中的卡尔曼滤波发散。同时输出数据由于受到随机误差的影响,会导致数据的平滑性能变差。提出了一种多普勒计程仪的数据降噪算法,该算法首先利用改进的中值滤波方法去除数据中的野点,再利用小波阈收缩去噪算法去除随机误差。仿真结果表明,与传统的中值滤波相比,该算法能极大地提高处理增益,并且有很高的应用价值。     

包装过程称量信号处理方法研究

目的 为提高包装过程定量称量精度,结合卡尔曼滤波算法和模糊控制原理设计一种称量信号处理方法。方法 定量称量控制系统一般由触摸屏、控制器、称量传感器、变频器等电气设备组成。以传感器信号处理为主要研究对象,提出一种改进卡尔曼滤波算法。采用卡尔曼滤波器实现称量信号中随机噪声的处理。利用模糊控制器来实时监测卡尔曼滤波每次更新后实际方差和理论方差的差值。最后,进行实验研究。结果 实验结果表明,改进卡尔曼滤波的实际性能比较理想,滤波处理前,称量误差最大可以达到2.5%;经滤波处理后,最大称量误差只有0.26%。结论 所述信号处理方法可以有效地降低称量信号噪声,提高称量精度。     

基于模糊自适应Kalman滤波的机械手动态图像雅可比矩阵辨识

提出了一种改进的基于模糊自适应Kalman滤波的动态图像雅可比矩阵辨识方法.该方法在机器人参数和滤波参数未知而且视觉成像模型动态变化的情况下,通过模糊逻辑自适应控制器在线监测滤波残差均值和残差协方差误差,对过程噪声参数Q和量测噪声参数R进行自适应调节,实现了未知环境下动态图像雅可比矩阵的稳定辨识.通过微装配机械手运动实验验证了该方法的有效性.     

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of traditional ideal lowpass filter. In detail, we reassess the existing low-rank denoising method with adaptive regularizer learning (ARLLR) from the view of graph filtering. A shrinkage approach subsequently is presented on the graph frequency domain, where the components of noisy image are adaptively decreased in each band by calculating their component significances. As a result, it makes the proposed graph filtering more explainable and suitable for denoising. Meanwhile, we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method. Therefore, ARLLR can be treated as a special form of graph filtering. It not only enriches the theory of graph filtering, but also builds a bridge from the low-rank methods to the graph filtering methods. In the experiments, we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix. The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.     

一种抑制卡尔曼滤波发散的实时数据处理方法

由于水声环境的复杂性和水声信道的时空变特性及水下航行载体的机动性,水声定位系统测量的弹道样点野值较多,平滑性差。介绍了一种野值的自动剔除和卡尔曼滤波递推处理方法,克服了滤波发散。文中选取距离D的倒数作为状态变量,使得1／D是近似线性变化的,此时量测方程的误差也近似是线性的,卡尔曼滤波器的表现是稳定的,并且是渐近无偏的。卡尔曼滤波的递推形式,滤波增益矩阵Kk的离线计算出,Qk和Rk值选取固定植,野值设定门限自动剔除,使滤波器收敛和稳定时间短,实现了对快速目标的跟踪和滤波输出,没有出现发散现象。该方法的特点是实时性好,对快速目标具有良好的跟踪能力,而且能达到工程上应用的精度要求。     

A real-time filtering method for the drift data of a laser Doppler velocimeter

To effectively reduce the random drift of a laser Doppler velocimeter (LDV), a real-time filtering model is presented for filtering the drift data of an LDV, which is a combination of the metabolic grey model (1, 1) and the metabolic time series model AR (2). The basic principle of the metabolic grey-time series model is introduced in detail first. Then, the model is established for the static and dynamic drift data, and a Kalman filter is used to filter the drift data based on the model. The variance analysis method and the Allan variance method are employed to analyse the static drift data. The dynamic drift data are also compared before and after being modelled and filtered. The results demonstrate that the metabolic grey-time series method cannot only effectively reduce the static random drift of an LDV, but can also reduce the dynamic random drift in real time.     

基于RFF及GWO-PF的锂电池SOC估计

为了提高锂电池剩余电量估计的准确性,提出一种在线参数辨识与改进粒子滤波算法相结合的锂电池SOC估计方法。针对粒子滤波中的粒子退化问题,引入灰狼算法,利用灰狼算法较强的全局寻优能力优化粒子分布,保证粒子多样性,有效抑制粒子退化现象,提高滤波精度。采用带遗忘因子的递推最小二乘法实时更新模型参数,并与改进粒子滤波算法交替运行,进一步提高SOC的估计精度。实验结果表明,改进算法的平均估计误差始终保持在±0.15%以内,相比扩展卡尔曼滤波与无迹卡尔曼滤波算法,在电池SOC估计上有更高的估计精度与稳定性。     

闪光CCD图像的中值-非线性扩散滤波

根据闪光CCD图像的特点,提出了一种中值-非线性扩散滤波(Median-NonlinearDiffusionFiltering,简称MNDF)方法。该方法采用中值预滤波来估计图像的真实边缘,通过求解偏微分方程(PartialDifferentialEquation,简称PDE)来进行非线性扩散滤波,充分发挥了中值滤波和非线性扩散滤波的优势,能更好地消除噪声、保护边缘。实验结果表明,在高斯噪声和脉冲噪声同时存在的情况下,MNDF方法取得的滤波效果较P-M方案和Catte方案要好,信噪比改善因子提高3~5倍,均方误差减小1.3~2.7倍。对闪光照相CCD图像取得了很好的消噪声结果,保护了边缘信息。