Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation

Guangfeng Wang; Linfeng Gou; Yingzhi Huang; Yingxue Chen

doi:10.1109/ICMAE59650.2023.10424042

Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation

Guangfeng Wang, Linfeng Gou, Yingzhi Huang, Yingxue Chen

School of Power and Energy

Northwestern Polytechnical University Xian

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

Abstract

In this paper, we investigate the problem of state estimation for a class of non-linear systems with non-Gaussian measurement noise. Based on the maximum correntropy criterion (MCC), an adaptive maximum correntropy unscented kalman filter (AMCUKF) is derived by introducing a weighted combined cost function and an adaptive kernel function bandwidth. The filter solves the numerical problem of the existing maximum correntropy unscented Kalman filter (MCUKF) when the measured value contains large outliers and the problem of performance degradation caused by improper kernel bandwidth selection. Finally, taking the aero-engine state estimation problem as an example, the filtering performance of different filters is compared, which shows that the filter proposed in this paper has advantages in dealing with non-linear and nonGaussian systems.

Original language	English
Title of host publication	2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	230-236
Number of pages	7
ISBN (Electronic)	9798350340327
DOIs	https://doi.org/10.1109/ICMAE59650.2023.10424042
State	Published - 2023
Event	14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023 - Porto, Portugal Duration: 18 Jul 2023 → 21 Jul 2023

Publication series

Name	2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023

Conference

Conference	14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023
Country/Territory	Portugal
City	Porto
Period	18/07/23 → 21/07/23

Keywords

adaptive
maximum correntropy criterion
non-Gaussian noise
unscented kalman filter

Access to Document

10.1109/ICMAE59650.2023.10424042

Cite this

Wang, G., Gou, L., Huang, Y., & Chen, Y. (2023). Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation. In 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023 (pp. 230-236). (2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMAE59650.2023.10424042

Wang, Guangfeng ; Gou, Linfeng ; Huang, Yingzhi et al. / Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation. 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 230-236 (2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023).

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title = "Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation",

abstract = "In this paper, we investigate the problem of state estimation for a class of non-linear systems with non-Gaussian measurement noise. Based on the maximum correntropy criterion (MCC), an adaptive maximum correntropy unscented kalman filter (AMCUKF) is derived by introducing a weighted combined cost function and an adaptive kernel function bandwidth. The filter solves the numerical problem of the existing maximum correntropy unscented Kalman filter (MCUKF) when the measured value contains large outliers and the problem of performance degradation caused by improper kernel bandwidth selection. Finally, taking the aero-engine state estimation problem as an example, the filtering performance of different filters is compared, which shows that the filter proposed in this paper has advantages in dealing with non-linear and nonGaussian systems.",

keywords = "adaptive, maximum correntropy criterion, non-Gaussian noise, unscented kalman filter",

author = "Guangfeng Wang and Linfeng Gou and Yingzhi Huang and Yingxue Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023 ; Conference date: 18-07-2023 Through 21-07-2023",

year = "2023",

doi = "10.1109/ICMAE59650.2023.10424042",

language = "英语",

series = "2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023",

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Wang, G, Gou, L, Huang, Y & Chen, Y 2023, Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation. in 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023. 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 230-236, 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023, Porto, Portugal, 18/07/23. https://doi.org/10.1109/ICMAE59650.2023.10424042

Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation. / Wang, Guangfeng; Gou, Linfeng; Huang, Yingzhi et al.
2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 230-236 (2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023).

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

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T1 - Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation

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AU - Chen, Yingxue

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N2 - In this paper, we investigate the problem of state estimation for a class of non-linear systems with non-Gaussian measurement noise. Based on the maximum correntropy criterion (MCC), an adaptive maximum correntropy unscented kalman filter (AMCUKF) is derived by introducing a weighted combined cost function and an adaptive kernel function bandwidth. The filter solves the numerical problem of the existing maximum correntropy unscented Kalman filter (MCUKF) when the measured value contains large outliers and the problem of performance degradation caused by improper kernel bandwidth selection. Finally, taking the aero-engine state estimation problem as an example, the filtering performance of different filters is compared, which shows that the filter proposed in this paper has advantages in dealing with non-linear and nonGaussian systems.

AB - In this paper, we investigate the problem of state estimation for a class of non-linear systems with non-Gaussian measurement noise. Based on the maximum correntropy criterion (MCC), an adaptive maximum correntropy unscented kalman filter (AMCUKF) is derived by introducing a weighted combined cost function and an adaptive kernel function bandwidth. The filter solves the numerical problem of the existing maximum correntropy unscented Kalman filter (MCUKF) when the measured value contains large outliers and the problem of performance degradation caused by improper kernel bandwidth selection. Finally, taking the aero-engine state estimation problem as an example, the filtering performance of different filters is compared, which shows that the filter proposed in this paper has advantages in dealing with non-linear and nonGaussian systems.

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Wang G, Gou L, Huang Y, Chen Y. Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation. In 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 230-236. (2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023). doi: 10.1109/ICMAE59650.2023.10424042

Adaptive Maximum Correntropy Unscented Kalman Filter for Aero-Engine State Estimation

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