Multiple Extended Target Tracking Based On Glmb Filter And Gibbs Sampler

In this paper, a new multiple extended target tracking learning algorithm based on labelled random finite sets (LRFS) framework is proposed to estimate the number, shape and state of extended targets under clutter conditions. The algorithm mainly includes two aspects: multi-extended target dynamic modeling and multi-extended target tracking estimates. Firstly, a finite mixture model (FMM) of extended target is established under the generalized labelled multi-bernoulli (GLMB) filter. Learning the parameters of finite mixture model by Gibbs sampling and Bayesian information criterion (BIC), and then equivalent point target measurements are used in place of the actual extended target measurements. Finally, the proposed ellipse approximation model is used to realize the estimation of the extended target shape. The simulation results show that the proposed algorithm can effectively track the multiple extended targets and obtain the shape of extended target.