eprintid: 10062808
rev_number: 18
eprint_status: archive
userid: 608
dir: disk0/10/06/28/08
datestamp: 2018-12-03 12:18:58
lastmod: 2021-09-18 21:49:09
status_changed: 2018-12-03 12:18:58
type: proceedings_section
metadata_visibility: show
creators_name: Chen, Z
creators_name: Heckman, C
creators_name: Julier, S
creators_name: Ahmed, N
title: Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Tuning, Optimization, Kalman filters, Bayes methods,
Linear programming, Stochastic processes,Technological innovation
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2018-09-06
date_type: published
publisher: IEEE
official_url: https://doi.org/10.23919/ICIF.2018.8454982
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1573080
doi: 10.23919/ICIF.2018.8454982
isbn_13: 9780996452762
lyricists_name: Julier, Simon
lyricists_id: SJULI23
actors_name: Julier, Simon
actors_id: SJULI23
actors_role: owner
full_text_status: public
publication: 2018 21st International Conference on Information Fusion, FUSION 2018
place_of_pub: Cambridge, UK
pagerange: 1072-1079
event_title: 2018 21st International Conference on Information Fusion (FUSION)
book_title: Proceedings of the 21st International Conference on Information Fusion (FUSION) 2018
citation:        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   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10062808/1/1807.08855v1.pdf