TY  - GEN
Y1  - 2018/09/06/
AV  - public
SP  - 1072
EP  - 1079
TI  - Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
PB  - IEEE
UR  - https://doi.org/10.23919/ICIF.2018.8454982
N2  - 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.
ID  - discovery10062808
A1  - Chen, Z
A1  - Heckman, C
A1  - Julier, S
A1  - Ahmed, N
KW  - Tuning
KW  -  Optimization
KW  -  Kalman filters
KW  -  Bayes methods
KW  - 
Linear programming
KW  -  Stochastic processes
KW  - Technological innovation
CY  - Cambridge, UK
ER  -