ARC: Adaptive Robust Joint State and Covariance Estimation

2026-06-18

AI Summary

Sensor measurements are often corrupted by outliers and non-Gaussian noise, leading conventional state estimators to produce biased and unreliable estimates. This work proposes an adaptive joint state and covariance estimation framework that uniquely integrates robust loss functions with covariance estimation. By combining norm-aware adaptive robust losses, iteratively reweighted least squares for state updates, and minimum weighted covariance determinant estimation within a block coordinate descent scheme, the method achieves self-tuning estimation without manual parameter tuning. Experimental results demonstrate that the approach accurately recovers inlier covariance in both Monte Carlo simulations and real-world ultra-wideband localization scenarios, achieving state estimation accuracy that matches or surpasses existing baseline methods.

Abstract

Sensor measurements are frequently corrupted by outliers and non-Gaussian noise. These imperfections in the sensor data can cause classical state estimators to generate biased and unreliable state and uncertainty estimates. Robust estimators reject or downweight outliers but do not perform measurement covariance estimation, whereas joint state and covariance estimators assume Gaussian residuals and fixed loss shape parameters. Integrating these two capabilities into a single framework is an opportunity to simultaneously estimate both state and covariance in the presence of outliers. This paper proposes a unified Block-Coordinate Descent framework that combines a norm-aware adaptive robust loss, an Iteratively Reweighted Least-Squares state update, and a Minimum Weighted Covariance Determinant covariance estimator, yielding a self-tuning joint state and covariance estimator. The framework is evaluated in a Monte-Carlo simulation and on real-world ultra-wideband localization experiments in cluttered non-line-of-sight environments. Results show that the proposed estimator consistently recovers the true inlier measurement covariance and matches or exceeds the state estimation accuracy of all baselines, without requiring any manual parameter tuning.

Problem

• outliers
• non-Gaussian noise
• state estimation
• covariance estimation
• robust estimation

Innovation

• Adaptive Robust Estimation
• Joint State and Covariance Estimation
• Iteratively Reweighted Least Squares
• Minimum Weighted Covariance Determinant
• Block-Coordinate Descent