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An EKF algorithm based on a skew-t measurement noise model

In many practical problems, measurement noise data are often skewed, heterogeneous, or containing outliers. The Gaussian or even Student-t measurement noise settings are often not fit to the request of system. A novel EKF filtering algorithm for nonlinear discrete state-space models with skewed Student-t measurement noise is presented. The algorithm makes use of the method of Variational Bayes to make approximation of the posterior of system state based on the known measurement noise parameters. State prediction uses the standard step of EKF. Simulation on the model of Signal tracking with skew-t measurement noise indicates the proposed algorithm outperforms the standard EKF for the set noise conditions.