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

Simo Särkkä Jouni Hartikainen

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arXiv · 1302.0681

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

Simo Särkkä Jouni Hartikainen

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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.

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BibTeX RIS

Simo Särkkä Jouni Hartikainen. 2013-02-04. Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering. https://arxiv.org/abs/1302.0681

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