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Robust Rauch–Tung–Striebel Smoothing Framework for Heavy-Tailed and/or Skew Noises
IEEE Transactions on Aerospace and Electronic Systems ( IF 2.797 ) Pub Date : 2019-05-06 , DOI: 10.1109/taes.2019.2914520
Yulong Huang; Yonggang Zhang; Yuxin Zhao; Lyudmila Mihaylova; Jonathon A. Chambers

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.
更新日期:2020-02-11

 

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