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Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines
(IEEE, 2018-10-04) Abdullahi Daniyan; Sangarapillai Lambotharan; Anastasios Deligiannis; Yu Gong; Wen-Hua Chen
In this paper, we propose a technique for the joint tracking and labeling 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 generalized labeled multi-Bernoulli filter framework. In particular, we developed a Poisson mixture variational Bayesian model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modeled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.