RCUKF: Data-Driven Modeling Meets Bayesian Estimation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913978784940032 |
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| author | Anurag, Kumar Azizi, Kasra Sorrentino, Francesco Wan, Wenbin |
| author_facet | Anurag, Kumar Azizi, Kasra Sorrentino, Francesco Wan, Wenbin |
| contents | Accurate modeling is crucial in many engineering and scientific applications, yet obtaining a reliable process model for complex systems is often challenging. To address this challenge, we propose a novel framework, reservoir computing with unscented Kalman filtering (RCUKF), which integrates data-driven modeling via reservoir computing (RC) with Bayesian estimation through the unscented Kalman filter (UKF). The RC component learns the nonlinear system dynamics directly from data, serving as a surrogate process model in the UKF prediction step to generate state estimates in high-dimensional or chaotic regimes where nominal mathematical models may fail. Meanwhile, the UKF measurement update integrates real-time sensor data to correct potential drift in the data-driven model. We demonstrate RCUKF effectiveness on well-known benchmark problems and a real-time vehicle trajectory estimation task in a high-fidelity simulation environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RCUKF: Data-Driven Modeling Meets Bayesian Estimation Anurag, Kumar Azizi, Kasra Sorrentino, Francesco Wan, Wenbin Machine Learning Systems and Control 93E11, 68T07 I.2.6; I.5.1 Accurate modeling is crucial in many engineering and scientific applications, yet obtaining a reliable process model for complex systems is often challenging. To address this challenge, we propose a novel framework, reservoir computing with unscented Kalman filtering (RCUKF), which integrates data-driven modeling via reservoir computing (RC) with Bayesian estimation through the unscented Kalman filter (UKF). The RC component learns the nonlinear system dynamics directly from data, serving as a surrogate process model in the UKF prediction step to generate state estimates in high-dimensional or chaotic regimes where nominal mathematical models may fail. Meanwhile, the UKF measurement update integrates real-time sensor data to correct potential drift in the data-driven model. We demonstrate RCUKF effectiveness on well-known benchmark problems and a real-time vehicle trajectory estimation task in a high-fidelity simulation environment. |
| title | RCUKF: Data-Driven Modeling Meets Bayesian Estimation |
| topic | Machine Learning Systems and Control 93E11, 68T07 I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2508.04985 |