A Variational Bayes Moving Horizon Estimation Adaptive Filter with Guaranteed Stability

TL;DR

This paper introduces a novel adaptive filtering method combining variational Bayesian inference with moving horizon estimation, providing guaranteed stability and improved accuracy in estimating states and unknown noise covariances in linear systems.

Contribution

It proposes a new VB MHE adaptive filter that jointly estimates states and noise covariances with stability guarantees, advancing adaptive filtering techniques.

Findings

Enhanced estimation accuracy over traditional filters

Guaranteed mean-square boundedness of estimation error

Effective target tracking demonstrated in simulations

Abstract

This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while preserving the stability guarantee of the Kalman filter. To this end, the full information estimation problem over a finite interval is firstly addressed. Then, a novel adaptive variational Bayesian (VB) moving horizon estimation (MHE) method is proposed, exploiting VB inference, MHE and Monte Carlo integration with importance sampling for joint estimation of the unknown process and measurement noise covariances, along with the state trajectory over a moving window of fixed length. Further, it is proved that the proposed adaptive VB MHE filter ensures mean-square boundedness of the estimation error with any number of importance samples and VB iterations, as well as for any window length. Finally, simulation results on a target tracking example demonstrate the effectiveness of the VB MHE filter with enhanced estimation accuracy and convergence properties compared to the conventional non-adaptive Kalman filter and other existing adaptive filters.