Analysis of Vector-Network-Analyzer-Based Power Sensor Calibration Method Application-Reference-Cited by-同舟云学术

Analysis of Vector-Network-Analyzer-Based Power Sensor Calibration Method Application

Published:2023-07-19 Issue:3 Volume:7 Page:21
ISSN:2410-390X
Container-title:Instruments
language:en
Short-container-title:Instruments

Author:

Danaci Erkan¹^ORCID,Bayrak Yusuf²,Cetinkaya Anil³⁴^ORCID,Arslan Murat¹,Sakarya Handan¹,Dogan Aliye Kartal¹,Tunay Gulsun⁵

Affiliation:

1. TUBITAK National Metrology Institute (UME), P.O. Box 54, Kocaeli 41470, Türkiye

2. Computer Engineering Department, Faculty of Engineering and Natural Science, Uskudar University, Altunizade Mh. Üniversite Sokağı No: 14, Istanbul 34662, Türkiye

3. Computer Engineering Department, Faculty of Engineering, Middle East Technical University, Üniversiteler Mahallesi, Dumlupınar Bulvarı No: 1, Ankara 06800, Türkiye

4. Computer Engineering Department, Faculty of Engineering and Natural Science, Iskenderun Technical University, Merkez Kampüs, Hatay 31200, Türkiye

5. Spark Kalibrasyon Hizmetleri A.Ş., M. Kemal Mahallesi, Dumlupınar Bulvarı No:274 B Blok. D: 104, Ankara 06510, Türkiye

Abstract

Radio Frequency (RF) power sensor calibration is one of the essential measurements in RF and microwave metrology. For a reliable and accurate power sensor calibration, there are various methods, such as the substitution method, the direct comparison transfer method (DCTM), and the vector network analyzer (VNA)-based calibration method (VBCM). The VBCM is a method that is derived from the DCTM. It is a preferred method since the VNA has a better measurement capability and has fewer connection requirements for measurement devices. In this study, the milestones and potential application errors of the VBCM are given by considering the connection mistakes, measurement faults, calculation errors, and control software coding problems. At the end of the power sensor calibration measurements with the VBCM, the model function components and the uncertainty calculation examples according to the GUM Bayesian method are also presented in this study. In addition, the advantages and disadvantages of the VBCM compared to the former methods are discussed in this study.

Funder

The Scientific and Technological Research Council of Turkey

Publisher

MDPI AG

Subject

Instrumentation

Link

https://www.mdpi.com/2410-390X/7/3/21/pdf

Reference17 articles.

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