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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter. / Fisch, Alex; Eckley, Idris; Fearnhead, Paul.
In: IEEE Transactions on Signal Processing, Vol. 70, 31.01.2022, p. 47-56.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fisch, A, Eckley, I & Fearnhead, P 2022, 'Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter', IEEE Transactions on Signal Processing, vol. 70, pp. 47-56. https://doi.org/10.1109/TSP.2021.3125136

APA

Fisch, A., Eckley, I., & Fearnhead, P. (2022). Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter. IEEE Transactions on Signal Processing, 70, 47-56. https://doi.org/10.1109/TSP.2021.3125136

Vancouver

Fisch A, Eckley I, Fearnhead P. Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter. IEEE Transactions on Signal Processing. 2022 Jan 31;70:47-56. Epub 2021 Nov 13. doi: 10.1109/TSP.2021.3125136

Author

Fisch, Alex ; Eckley, Idris ; Fearnhead, Paul. / Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter. In: IEEE Transactions on Signal Processing. 2022 ; Vol. 70. pp. 47-56.

Bibtex

@article{f14eb6c0b0394c83a7c82c0e0fc2e6a6,
title = "Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter",
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.",
keywords = "Kalman filter, Anomaly detection, Particle filtering, Robust filtering",
author = "Alex Fisch and Idris Eckley and Paul Fearnhead",
year = "2022",
month = jan,
day = "31",
doi = "10.1109/TSP.2021.3125136",
language = "English",
volume = "70",
pages = "47--56",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter

AU - Fisch, Alex

AU - Eckley, Idris

AU - Fearnhead, Paul

PY - 2022/1/31

Y1 - 2022/1/31

N2 - 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.

AB - 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.

KW - Kalman filter

KW - Anomaly detection

KW - Particle filtering

KW - Robust filtering

U2 - 10.1109/TSP.2021.3125136

DO - 10.1109/TSP.2021.3125136

M3 - Journal article

VL - 70

SP - 47

EP - 56

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

ER -