  The random matrix model is popular in extended object tracking, due to its
relative simplicity and versatility. In this model, the extended object state
consists of a kinematic vector for the position and motion parameters
(velocity, etc), and an extent matrix. Two versions of the model can be found
in literature, one where the state density is modelled by a conditional
density, and one where the state density is modelled by a factorized density.
In this paper, we present closed form Bayesian smoothing expression for both
the conditional and the factorised model. In a simulation study, we compare the
performance of different versions of the smoother.
