可用于混频器表征的非线性矢量网络分析仪

非线性矢量网络分析仪(NVNA)是一种针对射频微波器件非线性行为表征的测量装置,通过引入一个相位参考信号实现复杂被测对象的相位谱测量,目前主要用于功率放大器(PA)的表征和建模研究。提出了一种基于双相位参考结构的NVNA改进方案,并尝试用于混频器的非线性行为表征研究。该方案采用一个"步进式"的多频正弦信号作为"主"相位参考,以实现高频谱分辨率的多频段测量,同时引入第2个低频谱分辨率的"辅助"相位参考提供各测量频段的相位同步。该方案能够显著提高测量装置的信噪比,具备对多频段、"调制+谐波"复杂被测对象的测量表征能力。实验表明,该方案可以提供小于±0.2°相位谱测量稳定度,能够进行混频器在复杂调制状态下的相位和谐波失真特性分析。     

矢量网络分析仪8项误差模型和校正理论

对矢量网络分析仪S-参量误差模型的建立及校正理论进行了研究.给出了八项误差模型建立的基本理论.并根据此理论设计了一套利用标准元件对矢量网络分析仪S-参量测量进行校正的方法.简化了校正计算.     

矢量网络分析仪自动校准系统的设计与实现

矢量网络分析仪是无线通信领域常用的一种设备,现对该仪器的校准方法进行了归纳总结,提出了能对该仪器进行自动校准的系统的设计方案并加以实现,简述了系统的主要组成、自动校准软件的架构、软件流程图及各模块的功能,为矢量网络分析仪的自动校准提供了一个可行的参考方案。     

矢量网络分析仪动态范围的原位拓展方法

针对大带外抑制滤波器传输和反射特性的测试需求,提出了一种矢量网络分析仪动态范围的原位拓展方法。该方法在不改变仪器硬件架构的前提下,在测试链路中增加低噪声放大器,通过把微小信号放大来拓展动态范围,并采用互相关的信号处理算法,通过对两路测试数据进行多次运算进一步降低了仪器的噪声基底。为提升测试精度,解决因低噪声放大器的引入导致无法直接获取接收端口误差项的问题,设计了基于12项误差模型的两步校准方法,并完成了连接适配器参数的提取和去嵌入。最后,对两个不同频段的大带外抑制滤波器进行特性测量。试验结果表明,200 Hz中频带宽下动态范围可以达到130 dB,相对于常规仪器的动态范围可以提升20 dB以上,验证了方法的有效性。     

一种射频功放“谐波+互调”复杂非线性测量方法

提出了一种针对射频功放在调制信号激励下的"谐波+互调"复杂非线性测量方法。利用超低占空比Pulsed-RF信号驱动SRD脉冲发生器,该方法能够获得一种特殊的脉冲序列作为相位参考信号,并用于构建新型的非线性矢量网络分析仪NVNA测量装置。该相位参考信号能够在原有的各次谐波频点附件生成大量高频谱分辨率的调制谱线,从而保证NVNA实现"谐波+互调"复杂非线性的测量表征。实验表明,该方法在各次谐波频段可测量不少于180个有效频点,相位谱测量准确度可以达到±1°,能够获得射频功放在调制信号激励下的时频域行为特性。     

扩展矢量网络分析仪动态范围的几种方法

本文介绍了影响矢量网络分析仪动态范围的几个因素,分析了扩展矢量网络分析仪动态范围的几种简单易行的方法,能够对大动态范围器件的测试提供帮助。     

矢量网络分析仪时域功能研究

本文介绍了矢量网络分析仪时域测量的数学原理,分析了时域测量的具体过程以及不同的参数设定对变换结果的影响,比较了低通和带通两种测量模式各自的特点,阐述了窗函数在提高测量动态范围、改善测量结果中的作用。最后提出了如何利用矢量网络分析仪进行同轴电缆测试,并结合实例分析了矢量网络分析仪时域功能在电缆长度测试、电缆故障定位等方面的应用。     

矢量网络分析仪在时域测量中的应用

文章主要讲述了使用矢量网络分析仪(VNA)在时域测试中进行故障定位分析的应用,通过对几个实例的测量,说明了矢量网络分析仪在时域测量中的有效性和重要性。     

基于星座图设计的矢量调制误差计量方法研究

提出了一种基于星座图设计来实现对矢量信号发生器输出数字调制信号的调制误差进行准确设置的新方法,给出了方法的理论推导过程、方法的实现,以及实验验证.该方法可以用于实现对矢量信号分析仪在非零调制误差处测量准确度的校准,也可以用于数字调制误差参数量值比对传递标准的研制中.     

浅谈土壤重金属分析仪的校准方法

重金属离子含量是反映土壤污染的重要指标,重金属分析仪因其检测速度快,量值准确,检测限低,被广泛应用于快速检测与日常执法筛查工作中.该仪器的使用提高了应急处置的效率,在环境检测中发挥了越来越重要的作用.目前,国内并无该类仪器的校准规范,仪器的量值缺少溯源方法.本文结合实际计量工作,探讨了示值误差、测量重复性及检出限的校准...     

Multiport vector network analyzer calibration using the generalized 3-term error model

A generalized 3-term error model, where only three error matrixes are defined, is proposed for the calibration of n-port vector network analyzer (VNA) with n + 1 measurement channels. In this model, the node is replaced by the complex vector, and correspondingly the branch gain is represented by the complex square matrix. According to this error model, which also obeys the fundamental rules of flow graph, the formula for actual scattering matrix of an n-port DUT can be deduced. Finally, the actual S-parameters of a four-port device can be corrected and they are compared with the measurement results by Agilent VNA E5071B. The good agreement attests the precision of the calibration algorithm.     

Design of shared bus DSP board in vector network analyzer

Currently,the mainstream vector network analyzer employs embedded computer module with a digital intermediate frequency （IF） board to form a high performance windows platform.Under this structure,the vector network analyzer needs a powerful encoding system to arbitrate the bus acquirement,which is usually realized by field-programmable gate array （FPGA） chip.The paper explores the shared bus design method of the digital signal processing （DSP） board in network analyzer.Firsty,it puts an emphasis on the system structure,and then the shared bus communication method is described in detail; Finally,the advantages of the shared bus communication mechanism are summanzed.     

Optimal calibration for rotating analyzer ellipsometer

We have modeled most errors, which affect the measurement accuracy, with Jone’s matrix. From the simulation, we can characterize the errors and take good aids for selecting components and designing ellipsometer. The traditional residual method has good performance when there are only azimuth angle errors and extinction errors, but it has not good performance when there are other errors. We have proposed the optimal calibration method for overcoming the residual method. The optimal method selects error values to have the least square difference between the measured thickness and the simulated thickness. We can reduce the design variables to three, incident angle error, and azimuth angle errors of polarizer and analyzer. The optimization results are slightly different from the residual method, and have smaller standard deviation of errors than the residual method. The experiment shows good agreement with the simulations.     

Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint

In this paper, we aim to solve the control problem of nonlinear affine systems, under the condition of the input deadzone and output constraint with the external unknown disturbance. To eliminate the effects of the input deadzone, a Radial Basis Function Neural Network (RBFNN) is introduced to compensate for the negative impact of input deadzone. Meanwhile, we design a barrier Lyapunov function to ensure that the output parameters are restricted. In support of the barrier Lyapunov method, we build an adaptive neural network controller based on state feedback and output feedback methods. The stability of the closed-loop system is proven via the Lyapunov method and the performance of the expected effects is verified in simulation.     

System based on a ZVA-67 vector network analyzer for measuring high-frequency parameters of magnetic film structures

Thin magnetic films and multilayers are widely used in electronic devices and sensor systems, including systems for magnetic nondestructive testing and magnetic biodetection. Creating sensing elements of a new generation will necessitate the certification of film nanostructures. In this paper, a novel system is presented that allows automated measurement of parameters of thin ferromagnetic film structures at frequencies of 0.1 to 25 GHz in a magnetic field of up to 18 kOe.     

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.     

Robust state estimation for a class of uncertain nonlinear systems: Comparison of two approaches

In this paper, two approaches for robust state estimation of a class of Lipschitz nonlinear systems are proposed. First, a novel Unknown Input Observer (UIO) is designed without observer matching condition satisfaction. Then, an $H_{\infty }$ observer for approximate disturbance decoupling is proposed. Sufficient conditions for the existence of both proposed observers are derived based on a Lyapunov function. The achieved conditions are formulated in terms of a set of linear matrix inequalities (LMIs) and optimal gain matrices are obtained. The minimum values of the disturbance attenuation levels for both methods are obtained through solving optimization problems. Finally, the proposed approaches are compared by simulation studies of an automated highway system.     

光电测距仪检定方法研究进展

分析了光电测距仪的测距误差,介绍了光电测距仪室外检定方法和室内检定方法的国内外最新研究进展。室内检定方法是光电测距仪检定的发展趋势,因此重点分析了实现全室内检定存在的问题,提出了可能的解决思路。     

Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper.