  This paper proposes an improved prediction update for extended target
tracking with the random matrix model. A key innovation is to employ a
generalised non-central inverse Wishart distribution to model the state
transition density of the target extent; resulting in a prediction update that
accounts for kinematic state dependent transformations. Moreover, the proposed
prediction update offers an additional tuning parameter c.f. previous works,
requires only a single Kullback-Leibler divergence minimisation, and improves
overall target tracking performance when compared to state-of-the-art
alternatives.
