In this work, we propose a novel extended target tracking algorithm,
which is capable of representing a target or a group of targets with
multiple ellipses. Each ellipse is modeled by an unknown symmetric
positive-definite random matrix. The proposed model requires solving two
challenging problems. First, the data association problem between the
measurements and the sub-objects. Second, the inference problem that
involves non-conjugate priors and likelihoods which needs to be solved
within the recursive filtering framework. We utilize the variational
Bayes inference method to solve the association problem and to
approximate the intractable true posterior. The performance of the
proposed solution is demonstrated in simulations and real-data
experiments. The results show that our method outperforms the
state-of-the-art methods in accuracy with lower computational
complexity.