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

arXiv: https://arxiv.org/abs/1807.08855 · HTML: https://mlsanity.com/paper/1807.08855/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Implicit Maximum a Posteriori Filtering via Adaptive Optimization](https://mlsanity.com/paper/2311.10580/) (2023)
- [Local Bayesian Optimization for Controller Tuning with Crash Constraints](https://mlsanity.com/paper/2411.16267/) (2024)
- [Exact Consistency Tests for Gaussian Mixture Filters using Normalized Deviation Squared Statistics](https://mlsanity.com/paper/2312.17420/) (2023)
- [Rao-Blackwellized POMDP Planning](https://mlsanity.com/paper/2409.16392/) (2024)
- [Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits](https://mlsanity.com/paper/2302.12597/) (2023)
- [Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF](https://mlsanity.com/paper/2509.04213/) (2025)
- [Robust Unscented Kalman Filtering via Recurrent Meta-Adaptation of Sigma-Point Weights](https://mlsanity.com/paper/2603.04360/) (2026)
- [Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation](https://mlsanity.com/paper/2605.18704/) (2026)
- [Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification](https://mlsanity.com/paper/2606.02767/) (2026)

## Abstract (arXiv)

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.