Kalman filters are tracking and prediction algorithms based on Gaussian measurement errors and structural models. The Kalman filter performance may degrade if the measurement errors come from a thicker-tailed-than Gaussian distribution. In this report non-linear procedures are described which are based on Kalman-type models, but work with student-t measurement errors. Keywords: Kalman filter; Student-t measurement errors; Iterative reweighting procedure; Nonlinear filter; Biweight; Robust estimation