Outlier-robust Kalman Filtering through Generalised Bayes
Abstract
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering methods such as the extended and ensemble Kalman filter. We use the former to show robustness and the latter to ensure computational efficiency in the case of nonlinear models. Our method matches or outperforms other robust filtering methods (such as those based on variational Bayes) at a much lower computational cost. We show this empirically on a range of filtering problems with outlier measurements, such as object tracking, state estimation in high-dimensional chaotic systems, and online learning of neural networks.
Cite
@article{arxiv.2405.05646,
title = {Outlier-robust Kalman Filtering through Generalised Bayes},
author = {Gerardo Duran-Martin and Matias Altamirano and Alexander Y. Shestopaloff and Leandro Sánchez-Betancourt and Jeremias Knoblauch and Matt Jones and François-Xavier Briol and Kevin Murphy},
journal= {arXiv preprint arXiv:2405.05646},
year = {2024}
}
Comments
41st International Conference on Machine Learning (ICML 2024)