Variational Bayesian-based multiextended target tracking

This paper addresses the data association problem in multi-extended target tracking by proposing a variational Bayesianbased solution. The method models targets using random matrices. Within the variational Bayesian framework, association variables are used to establish data association between measurements and extended targets, and update equations are derived for estimating the motion and extended states of multiple extended targets. Simulation studies illustrate that the suggested data association technique efficiently allocates measurements, leading to efficient tracking of multiple extended targets.