Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
This addresses the tuning challenge for practitioners in navigation, tracking, and SLAM, though it is incremental as it applies an existing optimization technique to a specific domain.
The paper tackled the problem of tuning Kalman filter parameters, which is time-consuming and prone to local minima, by developing a Bayesian optimization method that uses NEES or NIS as objective functions to efficiently identify multiple minima with uncertainty quantification.
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.