Robust Gaussian Filtering using a Pseudo Measurement
This addresses robust filtering for sensors prone to outliers, but it is incremental as it modifies existing Gaussian filters rather than introducing a new paradigm.
The paper tackles the problem of sensor measurements contaminated by outliers, which are incompatible with Gaussian filters like the extended Kalman filter, by proposing to filter with a pseudo measurement derived from a feature function, resulting in effective outlier handling in simulations for linear and nonlinear systems.
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.