矢量网络分析仪的眼图交叉点估计方法

高强; 马世敏; 刘杰

doi:10.3969/j.issn.1000-1158.2023.05.16

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计量学报 ›› 2023, Vol. 44 ›› Issue (5) : 777-782. DOI: 10.3969/j.issn.1000-1158.2023.05.16

无线电、时间频率计量

矢量网络分析仪的眼图交叉点估计方法

高强^1,2,马世敏³,刘杰^1,2

作者信息 +

The Estimation Algorithm of Eye Diagram Cross-point for Vector Network Analyzer

GAO Qiang^1,2,MA Shi-min³,LIU Jie^1,2

Author information +

文章历史 +

摘要

眼图交叉点估计是眼图参数计算的重要步骤,直接影响到眼宽、峰峰值抖动等参数的计算精度。传统方法基于数据的统计特性,需要大量数据才能得到较精确的结果,由于矢量网络分析仪生成眼图较其他仪器更复杂,当数据量较大时需要采集数据的时间较长,影响仪器的使用效率。为了解决这一问题,提出了一种基于局部插值的眼图交叉点估计算法,在数据量较小时仍能估计出较精确的结果。实验表明,这种方法在估计精度为1.2×10-13s时所需的数据量仅为传统算法的1/4。

Abstract

Eye diagram cross-point estimation is an important step in eye diagram parameter calculation, which directly affects the calculation accuracy of eye width, peak-to-peak jitter and other parameters. The traditional method is based on the statistical characteristics of the data and requires a large amount of data to get more accurate results. Because the vector network analyzer generates eye diagrams more complicated than other instruments, it takes a long time to collect data when the amount of data is large, which affects the efficiency of the instrument. In order to solve this problem, an eye diagram cross-point estimation method based on local interpolation is proposed, which can still estimate more accurate results when the amount of data is small. Experiments show that when the estimation accuracy is 1.2×10-13s, the proposed method requires only 1/4 of the data of the traditional algorithm.

导出引用

高强,马世敏,刘杰. 矢量网络分析仪的眼图交叉点估计方法[J]. 计量学报. 2023, 44(5): 777-782 https://doi.org/10.3969/j.issn.1000-1158.2023.05.16

GAO Qiang,MA Shi-min,LIU Jie. The Estimation Algorithm of Eye Diagram Cross-point for Vector Network Analyzer[J]. Acta Metrologica Sinica. 2023, 44(5): 777-782 https://doi.org/10.3969/j.issn.1000-1158.2023.05.16

中图分类号： TB973

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