In this paper, an adaptive formulation of the sliding innovation filter (SIF) is presented. The SIF is a recently proposed estimation strategy that has demonstrated robustness to modeling errors and uncertainties. It utilizes a switching gain that is a function of the innovation (measurement error) and sliding boundary layer term. In this paper, a time-varying sliding boundary layer is derived based on minimizing the state error covariance. The resulting solution creates an adaptive formulation of the SIF. The adaptive SIF is applied on a linear aerospace system, and is compared with the well-known Kalman filter (KF) and the standard SIF. The results demonstrate the robustness of the new estimation strategy in the presence of modeling uncertainties and system faults.

中文翻译：

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滑动创新滤波器的自适应公式

在本文中，提出了滑动创新滤波器 (SIF) 的自适应公式。SIF 是最近提出的估计策略，它已证明对建模错误和不确定性具有鲁棒性。它利用作为创新（测量误差）和滑动边界层项的函数的开关增益。本文在最小化状态误差协方差的基础上推导出时变滑动边界层。由此产生的解决方案创建了 SIF 的自适应公式。自适应 SIF 应用于线性航空航天系统，并与著名的卡尔曼滤波器 (KF) 和标准 SIF 进行比较。结果证明了在存在建模不确定性和系统故障的情况下新估计策略的鲁棒性。