This study presents an unknown-but-bounded (UBB) model approach to robust extended Kalman filtering (REKF) problem for the simultaneous input and state estimation of non-linear systems with parametric uncertainties. An augmented non-linear state-space model is suggested to estimate the unknown input concurrently with the state variables without any delay. Due to more satisfactory physical assumptions and interpretations, the initial state vector and the disturbances are considered as UBB white processes, rather than the conventional stochastic white processes. Hence, they are modelled as ellipsoidal sets, and a recursive UBB-REKF is developed. The suggested algorithm aims to guarantee an optimal upper bound for the estimation error covariance considering the parametric uncertainties and the linearisation errors. Finally, the effectiveness and remarkable estimation accuracy of the proposed UBB-REKF is illustrated in a manoeuvring target tracking problem.

中文翻译：

---

输入未知的非线性系统的鲁棒扩展卡尔曼滤波：UBB模型方法

这项研究为具有参数不确定性的非线性系统的同时输入和状态估计提供了一种鲁棒扩展卡尔曼滤波（REKF）问题的未知但有界（UBB）模型方法。建议使用增强型非线性状态空间模型来同时估计未知输入和状态变量，而不会产生任何延迟。由于更令人满意的物理假设和解释，初始状态矢量和干扰被视为UBB白色过程，而不是常规的随机白色过程。因此，将它们建模为椭圆集，并开发了递归UBB-REKF。考虑到参数不确定性和线性化误差，建议的算法旨在保证估计误差协方差的最佳上限。最后，