Robust Gaussian Filtering using a Pseudo Measurement

TL;DR

This paper introduces a method to make Gaussian filters robust against outliers by using a pseudo measurement derived from a feature function, enabling effective outlier handling in various systems.

Contribution

It proposes a simple modification to Gaussian filters, filtering with a pseudo measurement instead of the physical measurement, to handle fat-tailed sensor models.

Findings

Effective outlier handling demonstrated in simulations.

Applicable to both linear and nonlinear systems.

Improves robustness of Gaussian filtering methods.

Abstract

Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters (such as the widely-used extended Kalman filter and unscented Kalman filter) are inherently incompatible with such fat-tailed sensor models. The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement. We derive such a feature function which is optimal under some conditions. Simulation results show that the proposed method can effectively handle measurement outliers and allows for robust filtering in both linear and nonlinear systems.