NIR-EKF: Normalized Innovation Ratio-Based EKF for Robust State Estimation

Abstract

Sensors deployed in real-world conditions often produce measurements corrupted by outliers due to model uncertainties, changes in the surrounding environment, and/or data loss. As a result, managing these outliers becomes crucial for state estimation to avoid inaccurate estimations and a reduction in the reliability of results. To address this issue, we introduce a novel form of extended Kalman filter (EKF) based on the maximum a posteriori (MAP) principle for scenarios where outliers simultaneously occur in multiple dimensions. For detecting outliers during the filtering process, we introduce a novel variant of the normalized innovation ratio (NIR) test and embed it within the EKF framework. Our approach enhances the estimation accuracy and computational efficiency of state estimation process even when data from several sensors simultaneously contain outliers.