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Robust Rauch–Tung–Striebel Smoothing Framework for Heavy-Tailed and/or Skew Noises.
- Source :
-
IEEE Transactions on Aerospace & Electronic Systems . Feb2020, Vol. 56 Issue 1, p415-441. 27p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189251
- Volume :
- 56
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Aerospace & Electronic Systems
- Publication Type :
- Academic Journal
- Accession number :
- 141729272
- Full Text :
- https://doi.org/10.1109/TAES.2019.2914520