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

@article{Fisch2020InnovativeAA,
  title={Innovative and Additive Outlier Robust Kalman Filtering With a Robust Particle Filter},
  author={Alexander T. M. Fisch and Idris Arthur Eckley and Paul Fearnhead},
  journal={IEEE Transactions on Signal Processing},
  year={2020},
  volume={70},
  pages={47-56}
}
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… 
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