Robust Inference for State-Space Models with Skewed Measurement Noise
Henri Nurminen, Tohid Ardeshiri, Robert Piché, Fredrik Gustafsson · IEEE Signal Processing Letters · 2015
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.