用数据驱动模型+贝叶斯估计，提升复杂系统状态预测精度
RCUKF: Data-Driven Modeling Meets Bayesian Estimation
- 用回声网络学习系统动态，替代传统数学模型
- 结合实时传感器数据修正模型偏差，抑制漂移
- 在高维混沌系统和车辆轨迹预测中表现优异
精确建模对众多工程与科学应用至关重要，但复杂系统往往难以获得可靠的过程模型。为此，我们提出一种新框架——基于无迹卡尔曼滤波的回声网络（RCUKF），将数据驱动的回声网络（RC）与贝叶斯估计中的无迹卡尔曼滤波（UKF）相结合。其中，回声网络从数据中直接学习非线性系统动力学，作为UKF预测步骤中的代理过程模型，用于高维或混沌状态下生成状态估计，弥补经典数学模型失效的问题。同时，UKF测量更新利用实时传感器数据校正数据驱动模型可能出现的漂移。我们在经典基准问题和高保真仿真环境下的实时车辆轨迹估计任务中验证了RCUKF的有效性。
原文摘要 · Abstract (English)
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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