Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter

Fisch, Alex and Eckley, Idris and Fearnhead, Paul (2022) Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter. IEEE Transactions on Signal Processing, 70. pp. 47-56. ISSN 1053-587X

Text (Supplementary Material)
Robust_Kalman_All_REVISION_3.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (744kB)

Text (Robust_Kalman_AuthorAccepted)
Robust_Kalman_All_REVISION_2.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (1MB)

Abstract

In this paper, we propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distribution of the hidden state. Furthermore, the particle sampling approach re-samples past states, which enables CE-BASS to handle innovative outliers which are not immediately visible in the observations, such as trend changes. The filter is computationally efficient as we derive new, accurate approximations to the optimal proposal distributions for the particles. The proposed algorithm is shown to compare well with existing approaches and is applied to both machine temperature and server data.