RCUKF: Data-Driven Modeling Meets Bayesian Estimation

• URL: http://arxiv.org/abs/2508.04985v1
• Date: Thu, 07 Aug 2025 02:41:43 GMT
• Title: RCUKF: Data-Driven Modeling Meets Bayesian Estimation
• Authors: Kumar Anurag, Kasra Azizi, Francesco Sorrentino, Wenbin Wan,
• Abstract summary: We propose a novel framework, reservoir computing with unscented Kalman filtering (RCUKF)<n>RCUKF integrates data-driven modeling via reservoir computing with Bayesian estimation through the unscented Kalman filter (UKF)<n>We demonstrate RCUKF effectiveness on well-known benchmark problems and a real-time vehicle trajectory estimation task in a high-fidelity simulation environment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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