  In this study, we propose a novel extended target tracking algorithm which is
capable of representing the extent of dynamic objects as an ellipsoid with a
time-varying orientation angle. A diagonal positive semi-definite matrix is
defined to model objects' extent within the random matrix framework where the
diagonal elements have inverse-Gamma priors. The resulting measurement equation
is non-linear in the state variables, and it is not possible to find a
closed-form analytical expression for the true posterior because of the absence
of conjugacy. We use the variational Bayes technique to perform approximate
inference, where the Kullback-Leibler divergence between the true and the
approximate posterior is minimized by performing fixed-point iterations. The
update equations are easy to implement, and the algorithm can be used in
real-time tracking applications. We illustrate the performance of the method in
simulations and experiments with real data. The proposed method outperforms the
state-of-the-art methods when compared with respect to accuracy and robustness.
