宇航计测技术 ›› 2024, Vol. 44 ›› Issue (5): 32-38.doi: 10.12060/j.issn.1000-7202.2024.05.05

• 量值传递技术 • 上一篇    下一篇

通信电缆转移阻抗精确测量和MCM不确定度评定

丁翔,朱梦涵,邓晓千*   

  1. 工业和信息化部电子第五研究所,广州 511300
  • 出版日期:2024-10-15 发布日期:2024-11-11
  • 作者简介:丁翔(1976-),男,高级工程师,硕士,主要研究方向:电子计量技术。
  • 基金资助:
    增城区创新创业领军团队项目(202102003)资助。

Accurate Measurement of Transfer Impedance and MCM Uncertainty Assessment of Communication Cables

DING Xiang,ZHU Menghan,DENG Xiaoqian*   

  1. The Fifth Electronics Research Institute of Ministry of Industry and Information Technology,Guangdong 511300,China
  • Online:2024-10-15 Published:2024-11-11

摘要: 为解决在高精度计量校准领域采用三同轴法测试通信电缆转移阻抗的过程中由于外筒分布参数、接头连接参数等因素严重影响量值溯源准确性的问题,通过分别测量夹具校准件和待测电缆S参数,运用网络级联算法计算屏蔽衰减,最后计算转移阻抗的方法能有效消除测量误差,从而提升转移阻抗测量准确性。由三同轴法测量原理出发,分析标准测试方案,详细论述二端口S矩阵与A矩阵换算和网络级联算法,推导出转移阻抗精确测量方案。最后通过实验进行了验证,并采用蒙特卡洛法(Monte Carlo Method,MCM)进行测量不确定度评定,评定结果为Ur=8%(k=2)。

关键词: 通信电缆, 转移阻抗, 网络, 级联, 不确定度, 蒙特卡洛法

Abstract: To solve the problem that the outer cylinder distribution parameters,joint connection parameters and other tests seriously affect the accuracy of value traceability in the process of testing communication cable transfer impedance using the tri-coaxial method in the field of high-precision measurement and calibration.By separately measuring the S parameters of the fixture calibrator and the cable under test,using the network cascade algorithm to calculate the shielding attenuation,and finally calculating the transfer impedance,the error can be effectively eliminated,aiming to improve the accuracy of the transfer impedance measurement.Starting from the measurement principle of the tri-coaxial method,the standard test plan is analyzed,the two-port S matrix and A matrix conversion and network cascade algorithm are discussed in detail,and an accurate measurement plan for transfer impedance is derived.It was verified through experiments and the Monte Carlo Method (MCM) was used to evaluate the measurement uncertainty,and the evaluation result was Ur=8%(k=2).

Key words: Communication cables, Transfer impedance, Network, Cascade, Uncertainty, Monte Carlo Method(MCM)

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