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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.02717 |
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| _version_ | 1866915597295550464 |
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| author | Impraimakis, Marios Smyth, Andrew W. |
| author_facet | Impraimakis, Marios Smyth, Andrew W. |
| contents | The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system parameters provide an estimation of the input. Secondly, the corrected with measurements states and parameters provide a final estimation. Importantly, it is demonstrated using the perturbation analysis that, a system with at least a zero or a non-zero known input can potentially be uniquely identified. This output-only methodology allows for a better understanding of the system compared to classical output-only parameter identification strategies, given that all the dynamic states, the parameters, and the input are estimated jointly and in real-time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An unscented Kalman filter method for real time input-parameter-state estimation Impraimakis, Marios Smyth, Andrew W. Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Systems and Control Audio and Speech Processing 68T05 (Learning and adaptive systems) I.2.6; I.2.8 The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system parameters provide an estimation of the input. Secondly, the corrected with measurements states and parameters provide a final estimation. Importantly, it is demonstrated using the perturbation analysis that, a system with at least a zero or a non-zero known input can potentially be uniquely identified. This output-only methodology allows for a better understanding of the system compared to classical output-only parameter identification strategies, given that all the dynamic states, the parameters, and the input are estimated jointly and in real-time. |
| title | An unscented Kalman filter method for real time input-parameter-state estimation |
| topic | Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Systems and Control Audio and Speech Processing 68T05 (Learning and adaptive systems) I.2.6; I.2.8 |
| url | https://arxiv.org/abs/2511.02717 |