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Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization

Chen, Z; Heckman, C; Julier, S; Ahmed, N; (2018) Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization. In: Proceedings of the 21st International Conference on Information Fusion (FUSION) 2018. (pp. pp. 1072-1079). IEEE: Cambridge, UK. Green open access

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Abstract

ISIF Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. Process noise covariance, pre-whitening filter models for non-white noise, etc. Conventional optimization techniques for tuning can get stuck in poor local minima and can be expensive to implement with real sensor data. To address these issues, a new 'black box' Bayesian optimization strategy is developed for automatically tuning Kalman filters. In this approach, performance is characterized by one of two stochastic objective functions: Normalized estimation error squared (NEES) when ground truth state models are available, or the normalized innovation error squared (NIS) when only sensor data is available. By intelligently sampling the parameter space to both learn and exploit a nonparametric Gaussian process surrogate function for the NEESINIS costs, Bayesian optimization can efficiently identify multiple local minima and provide uncertainty quantification on its results.

Type: Proceedings paper
Title: Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization
Event: 2018 21st International Conference on Information Fusion (FUSION)
ISBN-13: 9780996452762
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/ICIF.2018.8454982
Publisher version: https://doi.org/10.23919/ICIF.2018.8454982
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Tuning, Optimization, Kalman filters, Bayes methods, Linear programming, Stochastic processes,Technological innovation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10062808
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