Schmidt-Kalman Filter with Polynomial Chaos Expansion for State Estimation

Yang Yang, Han Cai, Baichun Gong, Robert Norman

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Errors due to uncertain parameters of dynamical systems can result in deterioration of state estimation performance or even filter divergence sometimes using a conventional Kalman filter algorithm. Even worse, these parameters cannot be measured accurately or are unobservable for many applications. Hence, estimating parameters along with state variables would not achieve satisfactory performance. To handle this problem, the Schmidt-Kalman filter (SKF) was introduced to compensate for these errors by considering parameters' covariance, with an assumption of only Gaussian distributions. This paper introduces a new SKF algorithm with polynomial chaos expansion (PCE-SKF). Within the framework of PCE, the dynamical system is predicted forward with an ability to quantify non-Gaussian parametric uncertainties as well. More specifically, the a priori covariance of both the state and parameters can be propagated using PCE, followed by the update step of SKF formulation. Two examples are given to validate the efficacy of the PCE-SKF. The state estimation performance by PCE-SKF is compared with the extended Kalman filter, SKF, unscented Kalman filter and unscented Schmidt-Kalman filter. It is implied that the covariance propagation using PCE leads to more accurate state estimation solutions in comparison with those based on linear propagation or unscented transformation.

源语言英语
主期刊名FUSION 2019 - 22nd International Conference on Information Fusion
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9780996452786
出版状态已出版 - 7月 2019
已对外发布
活动22nd International Conference on Information Fusion, FUSION 2019 - Ottawa, 加拿大
期限: 2 7月 20195 7月 2019

出版系列

姓名FUSION 2019 - 22nd International Conference on Information Fusion

会议

会议22nd International Conference on Information Fusion, FUSION 2019
国家/地区加拿大
Ottawa
时期2/07/195/07/19

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引用此

Yang, Y., Cai, H., Gong, B., & Norman, R. (2019). Schmidt-Kalman Filter with Polynomial Chaos Expansion for State Estimation. 在 FUSION 2019 - 22nd International Conference on Information Fusion 文章 9011327 (FUSION 2019 - 22nd International Conference on Information Fusion). Institute of Electrical and Electronics Engineers Inc..