A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model

Wei Huang; Yu Li; Hongpo Fu; Ruichen Ming; Weiguo Zhang

doi:10.1007/978-981-19-6613-2_588

A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model

Wei Huang, Yu Li, Hongpo Fu, Ruichen Ming, Weiguo Zhang

School of Automation

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In many practical applications, the prior statistics of process and measurement noises are inaccurate, and the heavy-tailed distributed noises are prone to occur due to outliers. To achieve state estimation under this situation, a new outlier-robust Kalman filter with Gaussian multi-scale mixture model (GMSMM-ORKF) is proposed. First, a GMSMM is presented to model the posterior probability density functions. Then, the hierarchical prior models on the mixture probability vectors and scale parameters are built. Furthermore, employing variational Bayesian inference, a GMSMM-ORKF is derived. Finally, the superiority of the filter is illustrated by the simulation and real data.

Original language	English
Title of host publication	Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
Editors	Liang Yan, Haibin Duan, Yimin Deng, Liang Yan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	6086-6096
Number of pages	11
ISBN (Print)	9789811966125
DOIs	https://doi.org/10.1007/978-981-19-6613-2_588
State	Published - 2023
Event	International Conference on Guidance, Navigation and Control, ICGNC 2022 - Harbin, China Duration: 5 Aug 2022 → 7 Aug 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	845 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Guidance, Navigation and Control, ICGNC 2022
Country/Territory	China
City	Harbin
Period	5/08/22 → 7/08/22

Keywords

Gaussian multi-scale mixture model
Heavy-tailed noise
State estimation
Variational Bayesian

Access to Document

10.1007/978-981-19-6613-2_588

Cite this

Huang, W., Li, Y., Fu, H., Ming, R., & Zhang, W. (2023). A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model. In L. Yan, H. Duan, Y. Deng, & L. Yan (Eds.), Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control (pp. 6086-6096). (Lecture Notes in Electrical Engineering; Vol. 845 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-6613-2_588

Huang, Wei ; Li, Yu ; Fu, Hongpo et al. / A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model. Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. editor / Liang Yan ; Haibin Duan ; Yimin Deng ; Liang Yan. Springer Science and Business Media Deutschland GmbH, 2023. pp. 6086-6096 (Lecture Notes in Electrical Engineering).

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title = "A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model",

abstract = "In many practical applications, the prior statistics of process and measurement noises are inaccurate, and the heavy-tailed distributed noises are prone to occur due to outliers. To achieve state estimation under this situation, a new outlier-robust Kalman filter with Gaussian multi-scale mixture model (GMSMM-ORKF) is proposed. First, a GMSMM is presented to model the posterior probability density functions. Then, the hierarchical prior models on the mixture probability vectors and scale parameters are built. Furthermore, employing variational Bayesian inference, a GMSMM-ORKF is derived. Finally, the superiority of the filter is illustrated by the simulation and real data.",

keywords = "Gaussian multi-scale mixture model, Heavy-tailed noise, State estimation, Variational Bayesian",

author = "Wei Huang and Yu Li and Hongpo Fu and Ruichen Ming and Weiguo Zhang",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",

year = "2023",

doi = "10.1007/978-981-19-6613-2_588",

language = "英语",

isbn = "9789811966125",

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Huang, W, Li, Y, Fu, H, Ming, R & Zhang, W 2023, A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model. in L Yan, H Duan, Y Deng & L Yan (eds), Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. Lecture Notes in Electrical Engineering, vol. 845 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 6086-6096, International Conference on Guidance, Navigation and Control, ICGNC 2022, Harbin, China, 5/08/22. https://doi.org/10.1007/978-981-19-6613-2_588

A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model. / Huang, Wei; Li, Yu; Fu, Hongpo et al.
Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. ed. / Liang Yan; Haibin Duan; Yimin Deng; Liang Yan. Springer Science and Business Media Deutschland GmbH, 2023. p. 6086-6096 (Lecture Notes in Electrical Engineering; Vol. 845 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In many practical applications, the prior statistics of process and measurement noises are inaccurate, and the heavy-tailed distributed noises are prone to occur due to outliers. To achieve state estimation under this situation, a new outlier-robust Kalman filter with Gaussian multi-scale mixture model (GMSMM-ORKF) is proposed. First, a GMSMM is presented to model the posterior probability density functions. Then, the hierarchical prior models on the mixture probability vectors and scale parameters are built. Furthermore, employing variational Bayesian inference, a GMSMM-ORKF is derived. Finally, the superiority of the filter is illustrated by the simulation and real data.

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Huang W, Li Y, Fu H, Ming R, Zhang W. A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model. In Yan L, Duan H, Deng Y, Yan L, editors, Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control. Springer Science and Business Media Deutschland GmbH. 2023. p. 6086-6096. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-19-6613-2_588

A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model

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Keywords

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