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

TL;DR

This paper introduces a Bayesian optimization method for automatically tuning Kalman filters, reducing manual effort and improving performance in data fusion applications by efficiently finding optimal parameters.

Contribution

It develops a novel black box Bayesian optimization approach that effectively tunes Kalman filter parameters using stochastic objective functions, handling local minima and uncertainty quantification.

Findings

Efficient automatic tuning of Kalman filters achieved.

Bayesian optimization outperforms traditional methods in parameter selection.

Improved filter performance with less manual intervention.

Abstract

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 NEES/NIS costs, Bayesian optimization can efficiently identify multiple local minima and provide uncertainty quantification on its results.