  In this work, we propose a tracking method for multiple extended targets or
unresolvable group targets based on the Variational Bayesian Inference (VBI).
Firstly, based on the most commonly used Random Matrix Model (RMM), the joint
states of a single target are modeled as a Gamma Gaussian Inverse Wishart
(GGIW) distribution, and the multi-target joint association variables are
involved in the estimation together as unknown information with a prior
distribution. A shape evolution model and VBI are employed to address the
shortcomings of the RMM. Through the VBI, we can derive the approximate
variational posterior for the exact multi-target posterior. Furthermore, to
demonstrate the applicability of the method in real-world tracking scenarios,
we present two potential lightweight schemes. The first is based on clustering,
which effectively prunes the joint association events. The second is a
simplification of the variational posterior through marginal association
probabilities. We demonstrate the effectiveness of the proposed method using
simulation experiments, and the proposed method outperforms current
state-of-the-art methods in terms of accuracy and adaptability. This manuscript
is only a preprint version, a completer and more official version will be
uploaded as soon as possible
