Abstract
To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student's t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.
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