Wang, M; Zhao, Y; Loh, T H; Xu, Q; Zhou, Y (2018) Efficient Uncertainty Evaluation of Vector Network Analyser Measurements Using Two-Tier Bayesian Analysis and Monte Carlo Method. In: 12th European Conference on Antennas and Propagation (EuCAP 2018), 9-13 April 2018, London, UK.

Full text not available from this repository.

Abstract

Based on the uncertainty propagation mechanism of VNA measurements, an efficient uncertainty evaluation method for VNA measurements using Bayesian analysis is proposed in this paper. In order to obtain a more credible uncertainty distribution assessment result, a two-tier Bayesian analytic process is carried out. The proposed method contains three steps. First, the posterior distribution of each uncertainty source of VNA calibrations is deduced by comprehensively use of prior information and the current sample information through the first-tier Bayesian analysis. In the second step, the obtained posterior distributions of uncertainty sources are taken into the Monte Carlo simulation of one-port VNA measurement uncertainties. At last, taking the results of the second step as the prior distribution of the secondary Bayesian evaluation, the final evaluation results of the measurement uncertainty can be given with the means, variances and skewness of the probabilistic distribution. The experiment results demonstrate the high-efficiency and reliability of this proposed method.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Electromagnetics > Wireless Communications
Divisions:	Engineering, Materials & Electrical Science
Last Modified:	02 Oct 2018 14:34
URI:	https://eprintspublications.npl.co.uk/id/eprint/8082