Daniyan, Abdullahi Lambotharan, Sangarapillai Deligiannis, Anastasios Gong, Yu Chen, Wen-Hua Bayesian multiple extended target tracking using labelled random finite sets and splines In this paper, we propose a technique for the joint tracking and labelling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. In particular, we developed a Poisson mixture variational Bayesian (PMVB) model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach. Multi-target tracking;Extended target tracking;B-splines;Variational Bayesian;Poisson mixture;Random finite sets;RFS;Labelled random finite sets;LMB;GLMB Bernoulli filter;Mechanical Engineering not elsewhere classified 2018-10-19
    https://repository.lboro.ac.uk/articles/journal_contribution/Bayesian_multiple_extended_target_tracking_using_labelled_random_finite_sets_and_splines/9224153