Advanced signal processing using automated uncertainty propagation - An educational approach

Abstract

Automated uncertainty propagation is a powerful tool that allows to focus on the interpretation and analysis of the uncertainty rather than on its calculation. A teaching concept based on the use of a software toolbox for automated uncertainty propagation have been previously developed. This work introduces Bayesian Linear Minimum Mean Square Estimation with automated uncertainty propagation, proposing an extension of the teaching concept, towards the use of uncertainty in sensor and data fusion. The toolbox provides the methods linearization, Unscented Transform and Monte Carlo for uncertainty propagation. Since the code for all three variants is essentially equivalent and compact, concepts as the Extended Kalman Filter and the Unscented Kalman Filter are easily implemented following the same approach as for the already introduced uncertainty quantification. The proposed approach simplifies the integration of advanced topics such as late fusion or posterior estimation of previous states in measurement science education.