用于数字条纹轮廓术的一种基于广义分析模型的后滤波方法

由于数字投影和采集系统的非线性失真,传统的方法通常需对采集到的条纹进行低通或带通滤波从而抑制高次谐波并获取基频分量。基于广义分析模型针对数字条纹轮廓术提出一种对测量后的物体形貌进行后滤波的算法,消除了非线性失真对测量结果所造成的影响并提高了数字条纹轮廓术的测量精度。论述了条纹轮廓术的原理和广义分析模型,分析推导出后滤波算法。仿真和实验结果表明,后滤波方法能够快速准确地实现基于数字投影的三维形貌重建。     

应用于增量式光电编码器的相关自适应滤波方法

为了改善增量式光电编码器的莫尔条纹光电信号质量,降低主计数信号中高次谐频的影响,提高信噪比达到输出稳定的正弦波形,本文深入地研究了莫尔条纹光电信号中的高频信号和噪音信号产生的原因及其相关自适应滤除方法.从宏观和微观两个方案分析光栅副的屏函数,研究莫尔条纹光电信号输出波形的不确定原因,并且提出采用相关自适应滤波方法将高频信号滤除保留原有基频信号.从相关自适应滤波的数学模型论证和推导该相关自适应滤波过程并讨论其自适应性.指出在设计该自适应滤波电路时需要重视的几个关键指标及其影响效果,完成相关自适应滤波的有效性分析.相关自适应滤波效果等价于一个中心频率为基频的窄带带通滤波器,当基频随机变化时其通带也会随之改变.通过相关自适应滤波技术较好地滤除计数信号中除基频外所有高频分量和放大电路所引入的加性高斯白噪音.实验证明：莫尔条纹光电信号的失真度被降低8倍相关自适应滤波方法能使莫尔条纹光电输出波形趋近于标准正弦波,获得更高的信噪比.     

Self-referenced method for optical path difference calibration in low-coherence interferometry

A simple method for the calibration of optical path difference modulation in low-coherence interferometry is presented. Spectrally filtering a part of the detected interference signal results in a high-coherence signal that encodes the scan imperfections and permits their correction. The method is self-referenced in the sense that no secondary high-coherence light source is necessary. Using a spectrometer setup for spectral filtering allows for flexibility in both the choice of calibration wavelength and the maximum scan range. To demonstrate the method's usefulness, it is combined with a recently published digital spectral shaping technique to measure the thickness of a pellicle beam splitter with a white-light source.     

应用于增量式光电编码器的相关自适应滤波方法

为了改善增量式光电编码器的莫尔条纹光电信号质量,降低主计数信号中高次谐频的影响,提高信噪比达到输出稳定的正弦波形,本文深入地研究了莫尔条纹光电信号中的高频信号和噪音信号产生的原因及其相关自适应滤除方法,从宏观和微观两个方案分析光栅副的屏函数,研究莫尔条纹光电信号输出波形的不确定原因,并且提出采用相关自适应滤波方法将高频...     

数字散斑条纹图的滤波方法

在现代数字散斑测量方法中，一般采用减模式来产生数字散斑条纹图，它是进一步图像处理的信息载体，同时不可避免地附带有大量的乘性噪声。为了保证测量精度，在进一步图像处理前必须对其进行降噪，选择合适的滤波方法显得尤为重要。在多年实际经验及阅读了大量文献的基础上，该文对国内外常用的数字散斑干涉条纹图滤波方法进行了分类阐述，选择了一些有代表性的方法予以实现，给出实验结果，并进行了对比分析。最后对数字散斑干涉条纹图滤波方法的发展方向给出了自己的看法。     

散斑干涉相位条纹图的频域滤波处理

为了解决在数字散斑干涉技术测量时,散斑干涉相位条纹图像中大量噪声对相位解包裹结果和精度产生严重影响的问题,介绍了一种条纹正余弦分解和频域低通滤波结合的方法,实现了散斑干涉相位条纹图的高精度滤波。该方法的基本思路是在对相位图像进行滤波处理前,先将相位图通过正余弦函数进行映射转换成两幅图,分别经过频域滤波,然后再合成为相位图。这种分解频域滤波方法可以在滤波的同时,有效保留相位跳变信息。实验结果表明：与传统的图像降噪方法相比,该方法能够在保留图像“尖峰”信息的基础上,较好地滤除图像中的散斑噪声,方法简单有效,有效解决了传统滤波方法应用在相位条纹图中,相图灰度信息丢失10%~40%的问题。     

Resolution Improvement in Optical Coherence Tomography with Segmented Spectrum Management

We demonstrate resolution improvement in an optical coherence tomography (OCT) system with multiple scans of segmented spectral bands and software signal processing. With an effective spectral full-width at half-maximum (FWHM) of 45 nm, centered at 950 nm, we compress the interference fringe FWHM from 31 μm down to 7 μm, which is smaller than the theoretical limit of 8.9 μm if a Gaussian spectral shape is assumed, through the combined effect of dispersion compensation and spectral shaping. With the amplitude adjustments of the OCT images of the segmented spectral bands, side-lobes can be significantly suppressed with a slight increase of the fringe envelope FWHM. With other amplitude adjustments, the envelope FWHM can be reduced to 5.6 μm at the expense of significant side-lobes.     

X荧光分析仪中数字基线估计的研究

在数字化X荧光分析仪中,不稳定的基线电压,会直接影响到仪器的性能,造成能量分辨率下降。基于此,利用卡尔曼滤波算法,对数字化后的X射线荧光信号进行基线估计。由于现有的经典卡尔曼滤波、简化sage-husa和改进sage-husa算法模型进行基线滤波的效果都不佳,因此有必要对现有的算法进行改进和优化。提出双重遗忘法,建立新型的基于sage-husa自适应卡尔曼滤波算法模型。实验结果表明,利用该数学模型进行基线滤波,取得了很好的滤波效果。避免了滤波发散和基线收敛缓慢的问题,实现了脉冲基线恢复,提高了仪器的能量分辨率。     

逼近积分点数下限的五阶容积卡尔曼滤波定轨算法

为了在保持滤波定轨精度不变的条件下提高定轨计算的实时性,提出一种新的逼近积分点个数下限的五阶容积卡尔曼滤波定轨算法.首先,采用一种数值容积准则对非线性函数的高斯加权积分进行近似,该准则所需的积分点个数仅比五阶代数精度容积准则积分点个数的理论下限多一个积分点,并在贝叶斯滤波算法框架下推导出本文算法的更新步骤.然后,给出实时定轨所需的状态方程和量测方程,在状态方程中考虑了J2项引力摄动和大气阻力摄动,在量测方程中利用坐标系转换推导了轨道状态与测量元素之间的非线性关系.仿真实验结果表明,本文所提算法在定轨精度方面与已有的五阶滤波算法相当,但所需的积分点个数最少,计算实时性最高,从而验证了本文算法的有效性.     

A five-point stencil based algorithm used for phase shifting low-coherence interference microscopy

The phase shifting technique is the most widely used approach for detecting the envelope in low coherence interferometry. However, if the phase shifts calibration contains errors, some parasitic fringe structure will propagate into the calculated envelopes and cause imprecision in the envelope peak detection. To tackle these problems, a five-point stencil algorithm is introduced into the phase shifting interference microscopy. Considering the amount of parasitic fringes, envelope peak detection and computational efficiency, the presented approach leads to satisfactory results in performance. In combination with a simple polynomial curve fitting method the proposed algorithm exhibits good performance on envelope peak detection in surface profiling. Both of the simulated results and the experimental results indicated that the presented approach can be taken as an alternative to the currently existing methods used for phase shifting low-coherence interference microscopy.     

基于集员估计的混沌通信窄带干扰抑制技术

基于混沌载波的有界性和最优定界椭球(OBE)准则,推导出了已知干扰信号模型参数的状态估计和未知干扰信号模型参数的自适应状态估计的干扰对消算法.与基于最小相空间体积(MPSV)的Kalman滤波和传统的递归最小二乘(RLS)算法相比,本算法具有选择更新特性,能在仅有少量数据参与更新的情况下达到与前者接近的性能,降低了计算量.该方法的性能通过在混沌参数调制(CPM)和差分混沌相移键控(DCSK)两种通信方式下对自回归(AR)型和单音两种窄带干扰的有效抑制得到了验证. 关键词： 最优定界椭球 混沌通信 干扰抑制 集员估计     

Automatic evaluation of photoelastic fringe constant by the nonlinear least-squares method

A nonlinear least-squares method was applied to determine the photoelastic fringe constant from isochromatic phase field obtained by digital phase-shifting photoelasticity. To perform automatic evaluation, not only the photoelastic fringe constant but the center coordinates of a disk under diametral compression are also determined simultaneously using the nonlinear least-squares method. A set of over-determined nonlinear equations is obtained to estimate the unknown parameters by an iterative procedure based on Newton–Raphson method. Simulation and experimental results show that the parameters are evaluated accurately from the isochromatic phase field obtained from the phase-shifting photoelasticity. The photoelastic fringe constant and the disk center coordinates can be evaluated easily, simply and automatically by the technique.     

基于随机共振进行弱信号探测的实验研究

非线性随机共振系统可利用噪声增强微弱信号检测的能力,为强噪声背景下微弱信号的检测开创了新方法.基于随机共振的基本原理设计了硬件电路系统,并将其应用于检测单频和多频微弱信号;通过输入模拟工程实际的带噪信号,采样所得的输出信号的频谱分析结果表明,利用随机共振技术可从强噪声背景下有效地提取出单频和多频弱信号.多频弱信号的有效提取拓展了基于随机共振原理的弱信号检测技术的应用领域,结合数字滤波处理技术有效地消除了低频噪声对信号识别的影响.基于随机共振的弱信号检测技术在信息识别与信息处理方面具有巨大的潜在的应用价值.     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

On the delay time of the net signal from a set of quasi-monochromatic transmitters

The waveform of the net signal from a set of closely spaced acoustic or electromagnetic transmitters generating identical (up to phase and amplitude factor) quasi-monochromatic signals is considered. If the partial signals are sufficiently long, the waveform of the envelope of the net signal from the set of transmitters turns out to be close to that of the partial signal envelope up to delay. The delay does not necessarily coincide with the time it takes for an optical (or acoustic) signal to travel the distance from the transmitter to the point of observation. At different sites of the space, the delay time may be real or complex. Its real part may be positive or negative and exceed, or be shorter than, the optical (acoustic) delay time. The delay strongly depends on the position of the point of observation (i.e., on whether it is located near a dark or bright interference fringe) and, in the case of short-wave signals, may vary considerably even at a slight displacement of the point of observation.     

Non-linear filtering of ultrasonic signals using neural networks

Structure noise from inhomogeneous micro-structures makes the detection of flaws present in highly scattering materials difficult. Several techniques have been applied to improve the signal-to-noise ratio (SNR) in order to make flaw detection easier. Linear filtering does not provide good results because both structure noise and flaw signal concentrate energy in the same frequency band. Non-linear filtering can be used to reduce the structure noise of ultrasonic signals. Therefore, neural networks are applied in this work for this purpose. In order to use neural networks for non-linear filtering, dynamic structures must be applied. The easiest way to implement a neural network with the capability of processing temporal patterns is to consider them spatial ones, applying the signal into a tapped delay line of finite extension, that is the input of a static neural network (for example, a multi-layer perceptron). In this work, a dynamic neural network has been built to filter ultrasonic signals with structure noise, and has been trained with the real-time back-propagation algorithm, using as inputs 3000 synthetic ultrasonic signals of 896 samples each. Target signals for training are the same as the ones used as inputs but without noise. The neural network is trained in order to generate as output the target signal when the noisy input one is applied. For testing the performance of the non-linear filter, a new set of 500 noisy signals has been used. The SNR improvement is about 6 dB average. The results show that this non-linear filtering method is quite useful as pre-processing stage in flaw detection systems.     

SINS初始对准中光纤陀螺EMD滤波

为了实现低成本SINS初始对准,降低对准过程复杂程度,提高系统对准精度,缩短对准时间,本文引入了EMD滤波技术。首先,采集IMU输出信号,根据EMD算法将信号分解为IMF簇,按照CMSE标准对信号进行重构,完成信号滤波处理;接着,按照AR模型对经EMD滤波前后的数据噪声进行建模;然后,分别利用原始信号和EMD降噪后信号进行SINS姿态粗对准;最后,根据IMU模型和SINS误差模型,采用零速对准方式,完成SINS精对准。实验结果表明:经EMD降噪后的信号粗对准精度为1.3°,精对准精度为0.87 mrad,精对准收敛时间为200 s。     

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.