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.
@article{arxiv.2508.04985,
title = {RCUKF: Data-Driven Modeling Meets Bayesian Estimation},
author = {Kumar Anurag and Kasra Azizi and Francesco Sorrentino and Wenbin Wan},
journal= {arXiv preprint arXiv:2508.04985},
year = {2025}
}
Comments
6 pages, 6 figures. Accepted at IFAC MECC 2025 (Modeling, Estimation and Control Conference)