Innovative And Additive Outlier Robust Kalman Filtering With A Robust Particle Filter
This work addresses robust filtering for applications like monitoring systems, but it appears incremental as it builds on existing particle filter methods with specific enhancements.
The paper tackles the problem of robust state estimation in the presence of both innovative and additive outliers, proposing CE-BASS, a particle mixture Kalman filter that effectively captures multi-modality and handles hidden outliers like trend changes, with results showing it compares well with existing approaches on machine temperature and server data.
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.