Gaussian Bayesian Networks for Estimating Stiff Continuous-Discrete Stochastic Systems with Ill-Conditioned Measurements

TL;DR

This paper presents a Gaussian Bayesian Network-based Extended Kalman Filter (GBN-EKF) that improves stability and accuracy in estimating stiff, ill-conditioned stochastic systems without matrix inversions, outperforming traditional EKF.

Contribution

The paper introduces GBN-EKF, a novel EKF extension using Bayesian networks that enhances stability and accuracy in challenging stochastic systems with ill-conditioned measurements.

Findings

GBN-EKF is stable without matrix inversions.

It achieves lower RMSE in stiff, ill-conditioned systems.

Comparable accuracy to EKF with improved stability.

Abstract

This paper introduces a Gaussian Bayesian Network-based Extended Kalman Filter (GBN-EKF) for non-linear state estimators on stiff and ill-conditioned continuous-discrete stochastic systems, with a further analysis on systems with ill-conditioned measurements. For most nonlinear systems, the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF) typically outperform the Extended Kalman Filter (EKF). But, in state estimation of stochastic systems, the EKF outperforms the CKF and UKF. This paper aims to extend the advantages of the EKF by applying a Gaussian Bayesian Network approach to the EKF (GBN-EKF), and analyzing its performance against all three filters. The GBN-EKF does not utilize any matrix inversions. This makes the GBN-EKF stable with respect to ill-conditioned matrices. Further, the GBN-EKF achieves comparable accuracy to the EKF in stiff and ill-conditioned stochastic systems, while having lower root mean squared error (RMSE) under these conditions.