Multiple extended-target tracking based on variational Bayesian cardinality-balanced multi-target multi-Bernoulli
Cuiyu Li · Control theory & applications · 2015
Because the performance of the conventional extended target-tracking declines greatly under the circumstance of unknown measurement noise covariance, we propose a new multiple extended target-tracking algorithm based on the variational Bayesian cardinality-balanced multi-target multi-Bernoulli(VB--CBMe MBer), and give its Gaussian mixture implementation. With unknown measurement noise covariance, the measurements of this algorithm are assumed to be produced by the measurement producers randomly distributing on the extended target. Then, the variational Bayesian(VB)approximation technique is applied to approximate joint probability density of the states of measurement producers, and the unknown measurement noise covariance. Their recursion forms are derived and are used to track measurement producers.Next, clustering algorithms are applied to the states of the tracked measurement producers to determine the states of the extended target. Simulation results show that the proposed algorithm can adaptively track unknown numbers of multiple extended targets with unknown measurement noise covariance. In addition, it has an improved precision when compared with conventional CBMe MBer algorithms.