This study proposes a robust extended Kalman filter (REKF) for discrete-time nonlinear systems with parametric uncertainties, unknown inputs, and correlated process and measurement noises. An augmented model is proposed to estimate the unknown inputs and system states simultaneously. The designed filter guarantees an upper bound on the error covariance of the estimation. It is robust against process and measurement noises, model uncertainties, and unknown inputs. Besides, the robust performance of the designed filter is evaluated. Finally, a realistic gas pipeline is simulated by OLGA multiphase flow simulation software. REKF and extended Kalman filter are compared to detect the pipeline's leakage and location. The results show the effectiveness of the proposed REKF.

中文翻译：

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存在未知输入和相关噪声的非线性系统的鲁棒扩展卡尔曼滤波

本研究针对具有参数不确定性、未知输入以及相关过程和测量噪声的离散时间非线性系统提出了一种稳健的扩展卡尔曼滤波器 (REKF)。提出了一种增强模型来同时估计未知输入和系统状态。设计的滤波器保证了估计的误差协方差的上限。它对过程和测量噪声、模型不确定性和未知输入具有鲁棒性。此外，评估了所设计滤波器的鲁棒性能。最后，通过OLGA多相流模拟软件模拟了一条真实的天然气管道。REKF和扩展卡尔曼滤波器进行比较，以检测管道的泄漏和位置。结果显示了所提出的 REKF 的有效性。