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.
Robust Rauch–Tung–Striebel Smoothing Framework for Heavy-Tailed and/or Skew Noises
IEEE Transactions on Aerospace and Electronic Systems ; 56 , 1 ; 415-441
2020-02-01
8202893 byte
Article (Journal)
Electronic Resource
English
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