基于源匹配优化的噪声系数自动测试系统

    Automated Noise Figure Test System Based onSource Matching Optimization

    • 摘要: 噪声系数作为T/R组件的重要技术指标之一,直接影响雷达系统的等效噪声系数。Y因子法测量噪声系数在实验室最为常用,但是Y因子法测试速度较慢,无法满足T/R组件自动测试需求,因而基于矢量网络分析仪(简称VNA, Vector Network Analyzer)的冷源法噪声系数测试成为噪声系数自动测试的主流方案。现有自动测试系统的噪声测试结果准确性和不确定度通常较差,存在较大的改进空间。本文从冷源法噪声系数的测试原理和噪声参数测试入手,分析源匹配对噪声系数测试的影响,提出了一种改良噪声系数自动测试系统的方案。经批量测试对比,改良后的噪声系数自动测试系统不确定度改善约一倍。

       

      Abstract: Noise figure, recognized as a critical technical parameter of T/R modules, governs the equivalent noise figure of radar systems. Whereas the Y-factor method predominantly employed in laboratory measurements fails to fulfill automated testing requirements for T/R modules, the cold-source method has been established as the standard solution. Automated test systems exhibiting suboptimal accuracy and consistency necessitate systematic optimization. Analysis of cold-source operational principles and noise parameters identifies source matching as a determinant factor in testing accuracy, leading to development of an optimized scheme for automated noise figure test systems. Comparative batch testing validates an approximate two-fold enhancement in consistency achieved by the optimized system.

       

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