Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs
Abstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-05-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12098 |
| _version_ | 1826831964402876416 |
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| author | Xinghua Liu Jianwei Guan Rui Jiang Xiang Gao Badong Chen Shuzhi Sam Ge |
| author_facet | Xinghua Liu Jianwei Guan Rui Jiang Xiang Gao Badong Chen Shuzhi Sam Ge |
| author_sort | Xinghua Liu |
| collection | DOAJ |
| description | Abstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method. |
| first_indexed | 2024-03-09T07:31:50Z |
| format | Article |
| id | doaj.art-fb12823cfd464f3cb7e85e56680cf771 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2025-02-16T10:09:59Z |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-fb12823cfd464f3cb7e85e56680cf7712025-02-03T01:29:25ZengWileyIET Signal Processing1751-96751751-96832022-05-0116335136510.1049/sil2.12098Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputsXinghua Liu0Jianwei Guan1Rui Jiang2Xiang Gao3Badong Chen4Shuzhi Sam Ge5School of Electrical Engineering Xi'an University of Technology Xi'an ChinaSchool of Electrical Engineering Xi'an University of Technology Xi'an ChinaDepartment of Electrical and Computer Engineering National University of Singapore Singapore SingaporeSchool of Electrical Engineering Xi'an University of Technology Xi'an ChinaSchool of Electronic and Information Engineering Xi'an Jiaotong University Xi'an ChinaDepartment of Electrical and Computer Engineering National University of Singapore Singapore SingaporeAbstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method.https://doi.org/10.1049/sil2.12098multiple suboptimal fading factorunbiased minimum varianceunknown inputsunscented Kalman filter |
| spellingShingle | Xinghua Liu Jianwei Guan Rui Jiang Xiang Gao Badong Chen Shuzhi Sam Ge Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs IET Signal Processing multiple suboptimal fading factor unbiased minimum variance unknown inputs unscented Kalman filter |
| title | Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs |
| title_full | Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs |
| title_fullStr | Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs |
| title_full_unstemmed | Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs |
| title_short | Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs |
| title_sort | robust strong tracking unscented kalman filter for non linear systems with unknown inputs |
| topic | multiple suboptimal fading factor unbiased minimum variance unknown inputs unscented Kalman filter |
| url | https://doi.org/10.1049/sil2.12098 |
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