  State estimation of dynamical systems from noisy observations is a
fundamental task in many applications. It is commonly addressed using the
linear Kalman filter (KF), whose performance can significantly degrade in the
presence of outliers in the observations, due to the sensitivity of its convex
quadratic objective function. To mitigate such behavior, outlier detection
algorithms can be applied. In this work, we propose a parameter-free algorithm
which mitigates the harmful effect of outliers while requiring only a short
iterative process of the standard update step of the KF. To that end, we model
each potential outlier as a normal process with unknown variance and apply
online estimation through either expectation maximization or alternating
maximization algorithms. Simulations and field experiment evaluations
demonstrate competitive performance of our method, showcasing its robustness to
outliers in filtering scenarios compared to alternative algorithms.
