基于 FPGA 的电磁金属探伤系统及其微弱信号处理

本文介绍了一种基于FPGA的电磁金属探伤系统,该系统用于研究电磁无损检测技术在金属板损伤探测上的应用。在该电磁金属探伤系统中,传感系统提供的测量感应信号为强噪声背景下的微弱信号。为了提高信号的检测精度,本文采用信号调理电路结合FIR数字滤波的方法,将基于FPGA的电磁成像金属探伤系统中微弱的测量感应信号从噪声中提取出来,仿真实验证明,该方案能为后续信号处理环节提供一个低噪声背景下的较强的感应信号。     

Adaptive IIR filter design for single sensor applications

The goal of this research was to investigate the theoretical design and physical implementation of a digital adaptive IIR filter to serve as an enhancement to the traditional active RC or passive RLC anti-aliasing filter. This all-digital filter will reside directly on the DSP engine. As explained in the paper, the adaptive IIR filter is designed to process an oversampled signal coming from a single sensor to reject noise in an acquisition system. Differentiation between the noise and the signal is obtained by exploiting the different auto-correlation functions of the two signals. In contrast to oversampling techniques employed in sampled data systems that are designed to relax the requirements of an analog anti-aliasing filter, this filter will track a signal in the frequency domain. Several power spectral density plots are given to illustrate the performance of the new filter. The results also indicate that the new filter performs well as compared to the Wiener filter in the stationary case     

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。     

Optimal Estimation of the Acceleration of a Car Under Performance Tests

In this paper, a recursive least-squares lattice (RLSL) adaptive filter was used to carry out the optimal estimation of the relevant signal coming from an accelerometer placed in car under performance tests. Here, the signal of interest is buried in a broadband noise background where we have little knowledge of the noise characteristics. In addition, due to the fact that the noise and the relevant information sometimes share the same or a very similar frequency spectrum, it is very difficult to cancel the noise that corrupts the relevant information without causing that information to deteriorate. The results of the experiment are satisfactory and, in order to compare classical filtering with optimal adaptive filtering, the signal coming from the accelerometer was also filtered by using a third-order lowpass digital Butterworth filter. The results of comparing the aforementioned filters show that the optimal adaptive filter is superior to the classical filter. Here, a significant improvement of 22.4 dB in the signal-to-noise ratio (SNR) at the RLSL adaptive filter output was achieved. However, the improvement in the SNR at the Butterworth filter output was 6.1 dB, which shows very clear that the optimal adaptive filter performs much better than the classical one     

基于VMD与共振稀疏分解的舰船辐射噪声窄带特征提取

为了获取实测舰船辐射噪声信号中有效的目标信息、提高低信噪比条件下目标信号的可分性,文章提出了结合变分模态分解(Variational Mode Decomposition,VMD)和共振稀疏分解(Resonance-based Sparsity Signal Decomposition,RSSD)的舰船辐射噪声信号特征提取方法。基于舰船辐射噪声信号具有一定的周期性而外界干扰具有随机性的特点,首先利用VMD自相关分析的方法重构信号,主要剔除带外噪声分量;然后采用RSSD算法基于信号共振属性的不同,进一步滤除带内噪声和瞬态干扰,实现对信号中周期性振荡成分的提取;最后提取信号的波形结构特征用于目标的分类识别。仿真信号与实测信号分析表明,该方法可以较好地滤除带内外噪声,增强舰船辐射噪声信号固有的窄带特征。多类舰船目标的分类实验结果表明,该方法可以有效提高低信噪比信号的可分性,有利于提高目标识别的性能。     

Real-time tracking of time-varying velocity using a self-mixing laser diode

A new method is proposed for estimating the time-varying velocity of a moving target with a low-cost laser sensor using optical feedback interferometry. A new algorithm is proposed to track velocity variations from real-time analysis of the output signal of a self-mixing laser diode. This signal is strongly corrupted by a multiplicative noise caused by the speckle effect, which occurs very often with noncooperative targets used in many industrial applications. The proposed signal processing method is based on a second order adaptive linear predictor filter, which enables us to track the digital instantaneous Doppler frequency, which is proportional to the velocity. A model of the laser diode output signal is proposed, and it is shown that the sensor and its associated algorithm have a global first-order lowpass transfer function with a cutoff frequency expressed as a function of the speckle perturbations, the signal to noise ratio and the mean Doppler frequency. Numerical as well as experimental results illustrate the properties of this sensor.     

基于方程误差法的IIR滤波有源噪声控制算法

在前馈有源噪声控制系统中会存在声反馈现象:次级声源产生的声信号反馈至参考传感器处.采用IIR滤波器能更好匹配有零极点的最优解,减弱声反馈影响.为克服FvLMS算法全局收敛性差的缺陷,提出采用比方程误差法(EEM)的计算量更小的EEM-FvLMS算法.在考虑声反馈的情况下,进行基于FvLMS算法和EEM-FvLMS算法的...     

Adaptive deblurring of noisy images

We propose a practical sensor deblurring filtering method for images that are contaminated with noise. A sensor blurring function is usually modeled via a Gaussian-like function having a bell shape. The straightforward inverse function results in the magnification of noise at high frequencies. To address this issue, we apply a special spectral window to the inverse blurring function. This special window is called the power window, which is a Fourier-based smoothing window that preserves most of the spatial frequency components in the passband and attenuates quickly at the transition band. The power window is differentiable at the transition point, which gives a desired smooth property and limits the ripple effect. Utilizing the properties of the power window, we design the deblurring filter adaptively by estimating the energy of the signal and the noise of the image to determine the passband and the transition band of the filter. The deblurring filter design criteria are (a) the filter magnitude is less than 1 at the frequencies where the noise is stronger than the desired signal (the transition band), and (b) the filter magnitude is greater than 1 at the other frequencies (the passband). Therefore the adaptively designed deblurring filter is able to deblur the image by a desired amount based on the estimated or known blurring function while suppressing the noise in the output image. The deblurring filter performance is demonstrated by a human perception experiment in which 10 observers are to identify 12 military targets with 12 aspect angles. The results of comparing target identification probabilities with blurred and deblurred images and adding two levels of noise to blurred and deblurred noisy images are reported.     

自动定量称量包装传感器信号去噪算法

目的为了有效滤除自动称量控制系统中的噪声信号,提升称量系统的稳定性和精确度,提出一种基于BP神经网络粒子滤波的称量信号去噪方法。方法在粒子滤波算法中映入了BP神经网络,利用BP神经网络的非线性映射特点,对权值进行分裂和选择,将观测值看作神经网络的目标信号,通过神经网络中的多次训练增大小权值粒子的权重,从而提高粒子滤波算法的多样性。结果仿真和实验结果表明,BP神经网络粒子滤波方法能有效滤除称量包装系统中的噪声信号,提升传感器信号品质。结论该滤波方法大大提升了称量系统的稳定性,有效提高了称量包装的精度,所述控制方法可以明显提高定量称量控制过程的稳定性、精确性以及鲁棒性。     

水下小孔径阵列远程目标检测方法研究

匹配滤波器频域自适应线谱增强方法是一种基于递归算法的非线性滤波技术,它大大提高了匹配滤波器的检测性能.针对当前该技术使用窄带信号作为发射信号存在可利用的带宽有限,不能充分发挥自适应线谱增强器性能的问题,文章提出将该技术与宽带信号相结合来检测远程目标.仿真显示,该方法在低信噪比条件下获得了较高的信噪比增益和检测概率.海试...     

组合声源管路信号失真的自适应修正方法研究

针对使用外置声源模拟汽车进气、排气噪声进行空气声传递路径分析的试验中,现有低频声源无法发出进排气时频率为30 Hz的低频噪声,同时由于低频声源体积过大需要过渡管道才能将噪声信号传输至排气尾管.为了解决声源系统引入过渡管道带来的声阻抗变化,提出了一种自适应预滤波的噪声修正方法.该方法采用最小均方误差自适应算法对系统逆传递...     

基于MMSE和谱峭度的滚动轴承故障诊断方法

共振解调作为滚动轴承常用的故障诊断方法,存在带通滤波器参数难以确定的缺点,针对此缺点以及滚动轴承早期故障信号信噪比低的问题,结合最小均方误差估计方法(MMSE)和基于谱峭度的共振解调方法,并将其应用于滚动轴承早期微弱故障诊断中。首先用MMSE方法抑制白噪声来提高信噪比,然后利用谱峭度自适应确定最优带通滤波器参数,最后对带通滤波后的信号进行能量算子解调谱分析,得出诊断结果。数字仿真信号和实验信号验证了该方法的有效性。     

参数扰动的RLC低通滤波器的随机共振

研究了RLC低通滤波器在电导受到非对称双值色噪声扰动时的随机共振现象．利用随机平均法和Shapiro—Loginov公式,得到了平均输出幅度增益的精确表达式．分析表明,在欠阻尼、临界阻尼和过阻尼RLC低通滤波器中,平均输出幅度增益对电导噪声的非对称性、相关时间、强度和信号频率都存在非单调依赖关系．适当的噪声和系统参数条件可以使有噪声时系统的平均输出幅度增益大于没有噪声时系统的平均输出幅度增益．噪声可以提高滤波器对高频信号的衰减率．恰当的条件可以获得平均输出幅度增益的最大值．     

石油管道超声检测信号的两级自适应噪声消除算法

针对石油管道缺陷超声检测信号的噪声消除问题, 研究了一种两级自适应噪声消除算法. 第一级自适应滤波器作为预处理级, 使信号获得较好的相关性和信噪比, 确保第二级自适应滤波器获得更优的性能. 实测超声信号两级自适应滤波结果表明: 两级自适应滤波算法能有效增强超声检测信号中的缺陷信号成分, 显著提高信噪比.     

一种基于滤波器组设计的多频带组合信号增强方法

针对低信噪比情况下未知多频带组合信号增强及其检测问题,提出了一种基于滤波器组设计的多频带组合信号增强方法。首先,依据耳蜗时频分解原理设计伽马通滤波器组,对传感器采集数据进行分频带处理;然后,根据信号与背景噪声在强度和稳定性上的差异性,对各频带数据进行方差归一化处理,建立判决统计量;最后,依据判决统计量对各频带数据进行判决处理,实现对多频带组合信号增强。数值仿真结果表明,在仿真条件下,该方法能够将数据中未知多频带组合信号信噪比提高6 dB以上,在信号频带未知情况下实现了对多频带组合信号的增强,提升了能量累积检测方法对未知多频带组合信号的检测效果。     

经验模态分解和空间滤波在两相流速度测量中的应用

在利用空间滤波和电容传感器测量两相流速度时,需要准确测量电容传感器输出信号的带宽.针对此问题提出一种利用经验模态分解算法来测量传感器带宽的方法.文章首先介绍电容传感器的空间滤波效应和经验模态分解的基本原理,并给出固体速度和电容传感器输出信号带宽之间的关系.然后将经验模态分解和平滑滤波器结合对测量信号进行平滑处理,测量处...     

Noncoherent spectral analysis of ADC using filter Bank

The aim of this paper is to propose a new spectral analysis method for an on-chip analog-to-digital converter (ADC) dynamic test. ADC characterization by spectral analysis has traditionally been done with discrete Fourier transform. This method imposes restrictions to optimize results; one of these is coherent sampling. Recently, some filter structures have been used for spectral analysis of a sinusoidal signal corrupted by harmonics and noise. In this paper, we present a new filter bank structure used for decomposing a signal into its main spectral components. The main application examined is ADC spectral parameter estimation, like signal-to-noise and distortion ratio, signal to noise ratio, total harmonic distortion, and so on, in noncoherent sampling. Computer simulations are used to demonstrate the performance of the proposed filter bank scheme. This structure is a promising built-in self-test (BIST) approach for ADC ICs.     

阵列测量中的卡尔曼滤波器算法

针对经典波束形成算法不具备实时性、占用存储空间大、计算速度慢等缺点,提出了基于卡尔曼滤波器的算法。这种算法将信号处理领域中现有的卡尔曼滤波器理论与阵列信号处理过程相结合,在频域内对声学阵列所采集到的数据进行迭代处理,不仅能够及时发现风洞测量中存在的各种问题,而且可以实时消除由测量环境所引起的各种误差。仿真结果表明,这种算法比经典波束形成算法收敛速度更快,不仅成像效果很好,而且能够对低速运动声源进行定位。此算法具备实时性,为风洞声源的实时定位提供了重要的算法选择。     

中值与小波消噪集成的转子振动信号滤波方法研究

对受强脉冲随机噪声干扰转子振动信号的滤波方法进行了研究。提出了一种将中值滤波与小波消噪算法集成的混合滤波器。其中,中值滤波用于滤除信号的强脉冲噪声分量,小波消噪平滑线性叠加在中值系列中的平稳随机噪声。结果表明,实施的滤波方法对该类信号的处理效果好,获取的转子本质振动信号保持了信号的光滑性。     

分形噪声中谐波信号的提取

本文提出一种分形噪声中谐波信号的提取算法。它利用小波变换对1／f噪声的白化作用,结合自适应自调谐滤波器组,在小波域实现了分形噪声与谐波信号的分离。仿真结果表明,该算法能够有效地抑制分形噪声,显著地提高信噪比。