Hidden-state unscented Kalman filter with unknown input for the joint identification of 3D structural parameters and unknown excitation.
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| Title: | Hidden-state unscented Kalman filter with unknown input for the joint identification of 3D structural parameters and unknown excitation. |
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| Authors: | Liu, Lijun1, Yang, Yahan1, Chen, Shoujin1, Wang, Shiyu1, Lei, Ying1,2, Zhou, Yujue3, Gong, Nan1 gongnan@xmu.edu.cn |
| Source: | Sound & Vibration. 2026, Vol. 60 Issue 3, p1-17. 17p. |
| Subjects: | Kalman filtering, System identification, Structural analysis (Engineering), Minimum variance estimation, State-space methods |
| Abstract: | The unscented Kalman filter with unknown input (UKF-UI) is an effective method for the identification of structural system and unknown excitation, but for three-dimensional multi-degree-of-freedom structures, the joint identification of structural state-parameter-unknown excitation often leads to high dimensions of extended state vector. To address this issue, a hidden-state unscented Kalman filter with unknown input is proposed for the joint identification of structural parameters and unknown excitation of three-dimensional structures. In the proposed method, only the time-invariant structural parameters are included in the structural state vector, while the displacements and velocities of all structural degrees of freedom are defined as hidden states and excluded from the state vector. This explicitly avoids the conventional extended state vector containing displacement, velocity and structural parameters. By reducing the state vector dimension, the identification of joint structural state-parameter is reduced to parameter-only identification. Moreover, the unbiased minimum variance estimation is used to achieve synchronous identification of unknown excitation. It avoids prior assumptions about unknown excitation and enhances the applicability in practical engineering. The identification of a three-dimensional frame structure under unknown excitation is used to verify the effectiveness of the proposed method. Through the observation of partial acceleration and displacement response data, structural element parameters of the three-dimensional structure and the unknown excitation acting on the three-dimensional structure can be identified. [ABSTRACT FROM AUTHOR] |
| Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195090147 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hidden-state unscented Kalman filter with unknown input for the joint identification of 3D structural parameters and unknown excitation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Lijun%22">Liu, Lijun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yahan%22">Yang, Yahan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Shoujin%22">Chen, Shoujin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Shiyu%22">Wang, Shiyu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lei%2C+Ying%22">Lei, Ying</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yujue%22">Zhou, Yujue</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Gong%2C+Nan%22">Gong, Nan</searchLink><relatesTo>1</relatesTo><i> gongnan@xmu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sound+%26+Vibration%22">Sound & Vibration</searchLink>. 2026, Vol. 60 Issue 3, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22System+identification%22">System identification</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+analysis+%28Engineering%29%22">Structural analysis (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Minimum+variance+estimation%22">Minimum variance estimation</searchLink><br /><searchLink fieldCode="DE" term="%22State-space+methods%22">State-space methods</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The unscented Kalman filter with unknown input (UKF-UI) is an effective method for the identification of structural system and unknown excitation, but for three-dimensional multi-degree-of-freedom structures, the joint identification of structural state-parameter-unknown excitation often leads to high dimensions of extended state vector. To address this issue, a hidden-state unscented Kalman filter with unknown input is proposed for the joint identification of structural parameters and unknown excitation of three-dimensional structures. In the proposed method, only the time-invariant structural parameters are included in the structural state vector, while the displacements and velocities of all structural degrees of freedom are defined as hidden states and excluded from the state vector. This explicitly avoids the conventional extended state vector containing displacement, velocity and structural parameters. By reducing the state vector dimension, the identification of joint structural state-parameter is reduced to parameter-only identification. Moreover, the unbiased minimum variance estimation is used to achieve synchronous identification of unknown excitation. It avoids prior assumptions about unknown excitation and enhances the applicability in practical engineering. The identification of a three-dimensional frame structure under unknown excitation is used to verify the effectiveness of the proposed method. Through the observation of partial acceleration and displacement response data, structural element parameters of the three-dimensional structure and the unknown excitation acting on the three-dimensional structure can be identified. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.59400/sv4116 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Kalman filtering Type: general – SubjectFull: System identification Type: general – SubjectFull: Structural analysis (Engineering) Type: general – SubjectFull: Minimum variance estimation Type: general – SubjectFull: State-space methods Type: general Titles: – TitleFull: Hidden-state unscented Kalman filter with unknown input for the joint identification of 3D structural parameters and unknown excitation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Lijun – PersonEntity: Name: NameFull: Yang, Yahan – PersonEntity: Name: NameFull: Chen, Shoujin – PersonEntity: Name: NameFull: Wang, Shiyu – PersonEntity: Name: NameFull: Lei, Ying – PersonEntity: Name: NameFull: Zhou, Yujue – PersonEntity: Name: NameFull: Gong, Nan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15410161 Numbering: – Type: volume Value: 60 – Type: issue Value: 3 Titles: – TitleFull: Sound & Vibration Type: main |
| ResultId | 1 |
