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Robust Inference for State-Space Models with Skewed Measurement Noise by Nurminen, Henri; Ardeshiri, Tohid; Piché, Robert; Gustafsson, Fredrik is a scholarly article available to read on EtoBox.

What is Robust Inference for State-Space Models with Skewed Measurement Noise about?

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.

Author

Nurminen, Henri; Ardeshiri, Tohid; Piché, Robert; Gustafsson, Fredrik

Published

2015

Language

EN