Robust Inference for State-Space Models with Skewed Measurement Noise

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.

5 pages, 7 figures. Accepted for publication in IEEE Signal Processing Letters

References in corpus (2)

• Distributions generated by perturbation of symmetry with emphasis on a multivariate skew $t$ distribution
• EMMIX-uskew: An R Package for Fitting Mixtures of Multivariate Skew t-distributions via the EM Algorithm

Cited by in corpus (7)

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• Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference
• Skew-t Filter and Smoother with Improved Covariance Matrix Approximation
• Robust Bayesian Filtering and Smoothing Using Student's t Distribution
• Ensemble Control for Stochastic Systems with Asymmetric Laplace Noises
• Approximate Recursive Identification of Autoregressive Systems with Skewed Innovations
• Skew-t inference with improved covariance matrix approximation