We propose a new robust recursive fixed-interval smoother for nonlinear systems under non-Gaussian process and measurement noises, i.e., the nominal Gaussian noise is polluted by large noise from unknown distributions. Taking advantage of correntropy in handling non-Gaussian noise, a robust Rauch–Tung–Striebel smoother is derived according to the maximum-correntropy-criterion-based cost functions with nonlinear functions linearized by their first-order Taylor series expansions, where two weights are utilized to adjust the estimation gains of forward filtering and backward smoothing, respectively. Simulation results demonstrate the effectiveness of the proposed smoother in the presence of various non-Gaussian process and measurement noises, especially the shot sequences and multimodal noise.

中文翻译：

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非线性和非高斯系统的最大熵Rauch-Tung-Striebel平滑器

我们为非高斯过程和测量噪声下的非线性系统提出了一种新的鲁棒递归固定间隔平滑器，即标称高斯噪声被未知分布中的大噪声所污染。利用熵来处理非高斯噪声，根据基于最大熵准则的成本函数导出了鲁棒的Rauch-Tung-Striebel平滑器，其中非线性函数通过一阶泰勒级数展开线性化，其中两个权重分别为用于分别调整前向滤波和后向平滑的估计增益。仿真结果表明，在存在各种非高斯过程和测量噪声（尤其是散粒序列和多峰噪声）的情况下，所提出的平滑器是有效的。