Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916486001459200 |
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| author | Yu, Yifan Xiu, Shengjie Palomar, Daniel P. |
| author_facet | Yu, Yifan Xiu, Shengjie Palomar, Daniel P. |
| contents | In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our algorithm addresses two significant limitations found in existing approaches: susceptibility to measurement noise outliers and difficulties in incorporating constraints into state estimation. By formulating constrained state estimation as an optimization problem and employing the Majorization-Minimization (MM) approach, our framework provides a unified solution that enhances the robustness of the Kalman filter. Experimental results demonstrate high accuracy and computational efficiency achieved by our proposed approach, establishing it as a promising solution for robust and constrained state estimation in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach Yu, Yifan Xiu, Shengjie Palomar, Daniel P. Signal Processing In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our algorithm addresses two significant limitations found in existing approaches: susceptibility to measurement noise outliers and difficulties in incorporating constraints into state estimation. By formulating constrained state estimation as an optimization problem and employing the Majorization-Minimization (MM) approach, our framework provides a unified solution that enhances the robustness of the Kalman filter. Experimental results demonstrate high accuracy and computational efficiency achieved by our proposed approach, establishing it as a promising solution for robust and constrained state estimation in real-world applications. |
| title | Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.11320 |