Current browse context:
stat.ML
Change to browse by:
References & Citations
Statistics > Machine Learning
Title: Outlier-robust Kalman Filtering through Generalised Bayes
(Submitted on 9 May 2024 (v1), last revised 28 May 2024 (this version, v2))
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.
Submission history
From: Gerardo Duran-Martin [view email][v1] Thu, 9 May 2024 09:40:56 GMT (6951kb,D)
[v2] Tue, 28 May 2024 07:03:49 GMT (6952kb,D)
Link back to: arXiv, form interface, contact.