Outlier-robust Kalman Filter in the Presence of Correlated Measurements

TL;DR

This paper introduces a new robust Kalman filtering method that effectively handles correlated measurement outliers by processing measurement errors separately, outperforming existing filters in correlated scenarios.

Contribution

The paper proposes a novel robust filtering framework that processes measurement errors separately, enhancing robustness against correlated outliers in measurements.

Findings

Outperforms existing robust filters with correlated measurements.

Performs comparably to existing filters with uncorrelated measurements.

Improves robustness in measurement outlier scenarios.

Abstract

We consider the robust filtering problem for a state-space model with outliers in correlated measurements. We propose a new robust filtering framework to further improve the robustness of conventional robust filters. Specifically, the measurement fitting error is processed separately during the reweighting procedure, which differs from existing solutions where a jointly processed scheme is involved. Simulation results reveal that, under the same setup, the proposed method outperforms the existing robust filter when the outlier-contaminated measurements are correlated, while it has the same performance as the existing one in the presence of uncorrelated measurements since these two types of robust filters are equivalent under such a circumstance.