Robust Inference for State-Space Models with Skewed Measurement Noise
Systems and Control
2015-06-30 v2 Computation
Abstract
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
Cite
@article{arxiv.1503.06606,
title = {Robust Inference for State-Space Models with Skewed Measurement Noise},
author = {Henri Nurminen and Tohid Ardeshiri and Robert Piché and Fredrik Gustafsson},
journal= {arXiv preprint arXiv:1503.06606},
year = {2015}
}
Comments
5 pages, 7 figures. Accepted for publication in IEEE Signal Processing Letters