Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering

TL;DR

This paper introduces a variational Bayesian method for jointly estimating states and time-varying noise covariances in non-linear state space models, utilizing Gaussian filtering techniques for efficient computation.

Contribution

The paper proposes a novel variational Bayes algorithm combined with Gaussian filtering for adaptive noise covariance estimation in non-linear Kalman filtering.

Findings

Effective in simulated applications

Utilizes efficient Gaussian integration methods

Improves covariance estimation accuracy

Abstract

This paper is considered with joint estimation of state and time-varying noise covariance matrices in non-linear stochastic state space models. We present a variational Bayes and Gaussian filtering based algorithm for efficient computation of the approximate filtering posterior distributions. The Gaussian filtering based formulation of the non-linear state space model computation allows usage of efficient Gaussian integration methods such as unscented transform, cubature integration and Gauss-Hermite integration along with the classical Taylor series approximations. The performance of the algorithm is illustrated in a simulated application.