  We address object tracking by radar and the robustness of the current
state-of-the-art methods to process outliers. The standard tracking algorithms
extract detections from radar image space to use it in the filtering stage.
Filtering is performed by a Kalman filter, which assumes Gaussian distributed
noise. However, this assumption does not account for large modeling errors and
results in poor tracking performance during abrupt motions. We take the
Gaussian Sum Filter (single-object variant of the Multi Hypothesis Tracker) as
our baseline and propose a modification by modelling process noise with a
distribution that has heavier tails than a Gaussian. Variational Bayes provides
a fast, computationally cheap inference algorithm. Our simulations show that -
in the presence of process outliers - the robust tracker outperforms the
Gaussian Sum filter when tracking single objects.
