Adaptive high-degree cubature Kalman filter in the presence of unknown measurement noise covariance matrix
| Autor: | Hong Xu, Huadong Yuan, Keqing Duan, Wenchong Xie, Yongliang Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Kalman filters
state estimation covariance matrices Bayes methods nonlinear filters variational techniques variational Bayesian method system state estimation nonlinear-state estimation problem unknown measurement noise covariance matrix adaptive high-degree cubature Kalman filter unknown covariance matrix online high-degree cubature rule adaptive HCKF unknown MN covariance matrix nonlinear systems radial integrals spherical integrals Engineering (General). Civil engineering (General) TA1-2040 |
| Zdroj: | The Journal of Engineering (2019) |
| Druh dokumentu: | article |
| ISSN: | 2051-3305 |
| DOI: | 10.1049/joe.2019.0389 |
| Popis: | Here, the authors address the state estimation problem of non-linear systems in the presence of unknown measurement noise (MN) covariance matrix. Recently, a high-degree cubature Kalman filter (HCKF) has been successfully used in the non-linear-state estimation problem with arbitrary degrees of accuracy in computing the spherical and radial integrals. However, the efficiency of the HCKF depends on a priori knowledge of the MN. To improve the performance of HCKF for non-linear systems with unknown MN covariance matrix, the authors proposed an adaptive HCKF, which combines the high-degree cubature rule with the variational Bayesian (VB) method to jointly estimate the system state and the unknown covariance matrix online. Experimental results demonstrate the effectiveness of the proposed filter. |
| Databáze: | Directory of Open Access Journals |
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