A novel robust Rauch–Tung–Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters, and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this paper is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers.

中文翻译：

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用于重尾噪声和/或倾斜噪声的鲁棒 Rauch-Tung-Striebel 平滑框架

基于广义高斯尺度混合 (GGScM) 分布，提出了一种新的鲁棒 Rauch-Tung-Striebel 平滑框架，用于具有重尾和/或歪斜噪声的线性状态空间模型。使用变分贝叶斯方法联合推断状态轨迹、混合参数和未知分布参数。因此，本文的主要贡献是在 GGScM 分发框架内统一结果。仿真和实验结果表明，所提出的平滑器比现有的平滑器具有更好的精度。