Radiometric Evaluation of Antenna-Feed Component Losses

Low-noise antenna systems are frequently used in conjunction with measurements of atmospheric and cosmic background noise at microwave frequencies. The input transmission line losses of these receiving systems must be precisely calibrated to insure proper identification of the portion of operating noise temperature attributable to the external environment. Although most components in an antenna line can be calibrated by conventional insertion-loss measurements, many feed component losses must be evaluated by means of nonstandard techniques. This paper describes a radiometric method for calibrating the loss of multimode antenna-feed components in which the field is linearly or circularly polarized. The method consists of measuring operating noise temperature, first with the components under evaluation installed and again after substitution by a waveguide section of known loss. Calibration and error analysis equations are derived and discussed. Application of the radiometric method, for the calibration of a mode-generator and quarter-wave plate polarizing section, resulted in a loss measurement of (0.0069 ±0.0016 pe)/dB.     

New developments for the sensitivity estimation in four-way calibration with the quadrilinear parallel factor model

Appropriate closed-form expressions are known for estimating analyte sensitivities when calibrating with one-, two-, and three-way data (vectors, matrices, and three-dimensional arrays, respectively, built with data for a group of samples). In this report, sensitivities are estimated for calibration with four-way data using the quadrilinear parallel factor (PARAFAC) model, making it possible to assess important figures of merit for method comparison or optimization. The strategy is based on the computation of the uncertainty in the fitted PARAFAC parameters through the Jacobian matrix. Extensive Monte Carlo noise addition simulations in four-way data systems having widely different overlapping situations are helpful in supporting the present approach, which was also applied to two experimental analytical systems. With this proposal, the estimation of the PARAFAC sensitivity for calibration scenarios involving three- and four-way data may be considered complete.     

基于最大相关熵准则的水下生物脉冲噪声消除方法

近海生物噪声表现为脉冲噪声,这一类噪声显著降低了水声通信系统的性能。结合水声信道的稀疏特性,提出一种基于最大相关熵准则的级联滤波器算法,该算法实现了脉冲噪声的消除及稀疏信道估计。第一级自适应滤波器完成对噪声的消除工作,第二级滤波器对去噪后的信号进行稀疏信道估计。该方法的优势在于无需知道信号和噪声的相关信息。仿真结果表明,提出的方法比单一的滤波器结构和传统信道估计算法性能更加优越。     

舰船宽带连续谱噪声的模拟

首先对舰船辐射噪声信号的时域特性和频域特性进行了分析讨论,根据噪声的连续谱特性,提出了一种用FIR滤波器实现宽带噪声信号模拟的方法。由于舰船宽带信号的连续谱在高频段具有-6dB/oct的衰减特性,因此该FIR滤波器的幅频响应也要与此一致,且为线性相位,即该滤波器的频率响应要具有特定的形状。采用了自适应方法很方便地实现了该特定频率响应FIR滤波器的设计,再将高斯白噪声信号通过该滤波器即实现宽带噪声信号的模拟。仿真结果表明,该方法较真实地模拟出任意给定频域特征的宽带连续谱噪声信号。     

Simulation of Adaptive Robust Estimation Procedures in Problems of Verification of Linear-Angular Measuring Instruments

The application is considered of adaptive robust estimation procedures to the problem of calibrating small-range measuring instruments. Results are reported from simulation of the adaptive method developed for verification of the given class of measuring instruments.     

Image restoration by adaptive-neighborhood noise subtraction

A new algorithm for image restoration in the presence of additive white Gaussian noise is presented. This algorithm is based on a new, adaptive method to estimate the additive noise. The basic idea in this technique is to identify uniform structures or objects in the image by use of an adaptive neighborhood and to estimate the noise and the signal content in these areas separately. The noise is then subtracted selectively from the seed pixel of the adaptive neighborhood, and the process is repeated at every pixel in the image. The algorithm is compared with the adaptive two-dimensional least-mean-squares and the adaptive rectangular-window least-mean-squares algorithms for noise suppression. The results from the application of these algorithms to synthesized images and natural scenes are presented along with mean-squared-error measures. The new algorithm performs better than the other two methods both in terms of visual presentation and mean-squared error.     

双自适应噪声抵消算法的实现

为有效剔除噪声,提高信噪比,提出一种基于双自适应的噪声抵消算法,包括自适应子带分解算法和自适应噪声抵消算法两部分。采用子带分解与噪声功率谱密度匹配的方法来对信号进行非均匀子带分解,根据噪声在子带中的分布进行有效滤波,对低噪或基本上无噪的子带不滤波,而对其它子带采用自适应滤波的算法。仿真对比表明,与传统的均匀子带自适应噪声抵消相比,计算量大大减小,其滤波效果也得到一定的改善。     

一种用于自适应有源噪声控制的在线次级通道建模方法

针对前馈式自适应有源降噪系统次级通道在线建模问题,首先分析比较已有的在线次级通道建模方法,给出了各自的优缺点。在此基础上提出了一种基于Fx LMS算法的在线次级通道建模方法,该方法使次级通道建模步长随干扰信号和建模白噪声信号功率自适应更新,降低了初级噪声对建模的不利影响,并利用误差信号相关值和初级噪声能量来更新控制滤波器步长,有效降低了突发随机噪声对系统稳定性的影响。计算机仿真结果表明,该方法显著提高了次级通道模型的精度和系统收敛速度,取得更好的降噪效果。     

基础振动影响下微g加速度计静态数学模型辨识

以从俄罗斯引进的低频微g加速度计校准装置为基础,分析了基础振动对微g加速度计校准结果的影响,给出了消除基础振动噪声的方法及在该种情况下加速度计静态数学模型的辨识方法.     

工业摄影测量系统校准技术研究进展综述

摄影测量技术的逐渐成熟使得工业摄影测量系统的应用范围越来越广泛。然而,目前国内还未建立起摄影测量系统的校准规范或标准。本文总结了国内外与摄影测量系统校准技术相关的研究与应用,并在此基础上研究摄影测量系统的校准技术和方法、研制专用的标准器具和校准装置,拟制定一套完整的工业摄影测量系统校准规范,为摄影测量系统的制造和使用提供计量基础保障。     

Speckle reduction in optical coherence tomography by adaptive total variation method

An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer’s hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.     

倾角传感器在线校准的激励源影响性分析

传感器是桥梁健康监测数据采集的关键部件,然而由于缺少合适的激励源和在线校准方法,在用桥梁健康监测系统传感器的计量性能难以在现场条件下得到有效验证。为探索利用行驶车辆的组合加载效应作为激励源进行桥梁健康监测传感器在线校准的可行性,提出了基于共源激励条件下序列数据分析的试验方法。以简支梁模型桥为基础,模拟了中心挠曲变形增量0～10mm,加载变化周期分别为5,10,15,20s的条件下,倾角传感器在线校准的激励条件,试验验证了激励源特性对校准结果稳定性的影响。统计学假设检验认为在显著性水平为0.05的条件下,激励源所引起的挠曲变形量、加载变化周期等对所试倾角传感器的校准结果无显著影响。     

自适应空域矩阵滤波在匹配场定位中的应用

匹配场定位问题中,为了不影响观测结果,需要抑制观测区域存在的干扰。广义空域滤波在抑制拖曳舰船噪声及类似已知干扰的问题上起到了很好的效果,但对于强干扰或未知干扰声源信息的情况,需要寻找一种新的方法。波束形成目标方位估计问题中的自适应空域矩阵滤波器设计方法可以用来解决这一问题。对自适应空域矩阵滤波器增加稳健性约束处理后,针对匹配定位问题做了大量仿真实验,比较了广义空域矩阵滤波器和自适应空域矩阵滤波器对干扰的抑制效果。实验结果表明,如果选取正确的通带范围,自适应空域矩阵滤波在匹配场定位应用中可以非常有效地抑制未知强干扰。     

基于三阶累积量及自适应滤波时延估计的管道定位方法

针对直接互相关被动时延估计法定位管道异常振动事件存在噪声干扰影响定位精度的问题,提出了基于三阶累积量及自适应滤波时延估计的管道异常事件定位方法。该方法对顺、反两路异常振动信号进行三阶自累积量和互累积量估计,抑制高斯相关噪声和对称分布噪声。然后利用自适应滤波时延估计算法对三阶自累积量和互累积量信号的时延进行迭代计算,在不依赖先验知识的情况下抑制非高斯相关噪声。经现场实验证明,该方法可以准确地对管道异常事件进行定位,对噪声具有很好的抑制作用,改善了直接互相关时延估计的性能。相对于直接互相关时延估计方法,相对定位误差由2.7%降低到0.6%,定位一致性提高了三倍,平均定位精度可达14m。     

Theoretical principles for establishing a hierarchy of dynamicaccuracy with the integral-square-error as an example

This paper presents the possibility of applying maximum dynamic errors to establish a hierarchy of measuring systems intended for dynamic measurements. To determine maximum errors, it is necessary to apply strictly defined calibrating signals. The shape analysis and necessary and sufficient conditions for such signals to exist are presented with the integral-square-error criterion as an example. Moreover, limitations imposed on calibrating signals depending on the low-pass, high-pass, or bandpass types of calibrated systems are also discussed. The values of the maximum errors determined by means of the signals mentioned above make it possible to create accuracy classes and the resulting hierarchy of dynamic accuracy, valid regardless of the measured signal shape. The solutions presented in the paper refer to both measuring analog systems and systems with analog-to-digital (A/D) conversion     

MISTRAL: a myopic edge-preserving image restoration method, with application to astronomical adaptive-optics-corrected long-exposure images

Deconvolution is a necessary tool for the exploitation of a number of imaging instruments. We describe a deconvolution method developed in a Bayesian framework in the context of imaging through turbulence with adaptive optics. This method uses a noise model that accounts for both photonic and detector noises. It additionally contains a regularization term that is appropriate for objects that are a mix of sharp edges and smooth areas. Finally, it reckons with an imperfect knowledge of the point-spread function (PSF) by estimating the PSF jointly with the object under soft constraints rather than blindly (i.e., without constraints). These constraints are designed to embody our knowledge of the PSF. The implementation of this method is called MISTRAL. It is validated by simulations, and its effectiveness is illustrated by deconvolution results on experimental data taken on various adaptive optics systems and telescopes. Some of these deconvolutions have already been used to derive published astrophysical interpretations.     

一种自适应模拟舰船噪声重构方法

提出了一种自适应模拟噪声重构方法(AMNR-Adaptive Modeling Noise Reconstruction)。该方法将噪声重构过程看作未知被控连续输入、输出系统,在已知系统输入、输出特性的情况下,采用自适应模拟技术建立并在线自适应修正噪声重构系统的传输函数,实现了通过高斯随机序列对舰船噪声的重构和任意长度重构噪声波形的连续输出。实测舰船噪声重构应用实例表明,本文所提方法重构误差小、实现方便,且重构噪声波形具有连续相位,从而解决了因重构噪声非连续输出带来的带内能量泄漏和功率谱随机波动等问题。     

邻域自适应选取的局部投影非线性降噪方法

局部投影降噪算法已广泛应用于非线性时间序列的分析中，但受邻域选取的影响较大。提出了一种按照自适应方式选取邻域大小的局部投影降噪算法。首先用时间延迟方法将一维时间序列重构到高维相空间。然后逐步增大每个待分析相点的领域大小，根据领域最大主方向变化过程中首次出现平稳阶段时，自适应地确定该相点的最优领域，最后再用局部几何投影的方法去除噪声成分。对洛伦兹信号和杜芬信号分别添加不同噪声水平的高斯白噪声，对领域自适应选取的局部投影算法与标准局部投影算法的降噪效果进行了比较。实验结果表明，自适应邻域选取方法，提高了局部投影算法的降噪能力和对领域参数的鲁棒性。     

基于小波收缩策略的一种新阈值去噪方法研究

分析了信号处理中相干噪声的特点,针对小波变换标准软阈值去噪法处理相干噪声能力相对差的弱点,介绍了一种基于SURERisk理论建立的自适应阈值去噪法,但其数字计算能力较差。基于同样理论,提出一种新的阈值函数,具有二阶导数和实际应用数字特性。并在此基础上确定了自适应阈值计算方法,建立了新的阈值去噪法,可用于去除相干噪声。计算机仿真表明,对于受相干噪声干扰的数字信号,该方法能较大提高信噪比,较好恢复信号。     

Fast wave-front reconstruction in large adaptive optics systems with use of the Fourier transform

Wave-front reconstruction with the use of the fast Fourier transform (FFT) and spatial filtering is shown to be computationally tractable and sufficiently accurate for use in large Shack-Hartmann-based adaptive optics systems (up to at least 10,000 actuators). This method is significantly faster than, and can have noise propagation comparable with that of, traditional vector-matrix-multiply reconstructors. The boundary problem that prevented the accurate reconstruction of phase in circular apertures by means of square-grid Fourier transforms (FTs) is identified and solved. The methods are adapted for use on the Fried geometry. Detailed performance analysis of mean squared error and noise propagation for FT methods is presented with the use of both theory and simulation.