Accurate Monte Carlo Uncertainty Analysis for Multiple Measurements of Microwave Systems

Abstract

Uncertainty analysis of microwave electronic measurements enables the quantification of device performance and aides in the development of robust technology. The Monte Carlo method is commonly used to attain accurate uncertainty analyses for complicated nonlinear systems. Combining multiple similar measurements, each with a Monte Carlo uncertainty analysis, allows one to incorporate the uncertainty given by their spread. In this paper, we compare two Monte Carlo sampling methods, illustrate that one method reduces the bias of averaged quantities, show how this impacts computed uncertainties, and highlight microwave applications for which this corrected method can be applied.