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
01.02.2020
8202893 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Minimum Error Entropy Rauch–Tung–Striebel Smoother
IEEE | 2023
|British Library Conference Proceedings | 2023
|SAE Technical Papers | 2023
|