Outlier-Insensitive Kalman Filtering: Theory and Applications

TL;DR

This paper introduces a parameter-free outlier-insensitive Kalman filtering method that models outliers as normal processes with unknown variance, enhancing robustness in noisy state estimation tasks.

Contribution

It presents a novel, simple iterative algorithm that improves Kalman filter robustness to outliers without requiring additional parameters.

Findings

Demonstrates robustness to outliers in simulations

Shows competitive performance in field experiments

Requires only a short iterative update process

Abstract

State estimation of dynamical systems from noisy observations is a fundamental task in many applications. It is commonly addressed using the linear Kalman filter (KF), whose performance can significantly degrade in the presence of outliers in the observations, due to the sensitivity of its convex quadratic objective function. To mitigate such behavior, outlier detection algorithms can be applied. In this work, we propose a parameter-free algorithm which mitigates the harmful effect of outliers while requiring only a short iterative process of the standard update step of the KF. To that end, we model each potential outlier as a normal process with unknown variance and apply online estimation through either expectation maximization or alternating maximization algorithms. Simulations and field experiment evaluations demonstrate competitive performance of our method, showcasing its robustness to outliers in filtering scenarios compared to alternative algorithms.