Variational Bayes Data Association Filter

We propose a sequential variational Bayes method, which is a recursive formulation of variational Bayes method, extended for online learning. We derived a novel data association filtering method for multiple targets, named variational Bayes data association filter (VBDAF). To estimate multiple targets´ states, data association is an important problem, when data don´t have unique labels and we can only associate data and targets probabilistically. EM algorithms or variational Bayes methods have been used for estimation problems with missing values such as data labels, but they are batch formulations. JPDAF have been widely used for multiple targets tracking. It is an extended filtering method based on sequential Bayes methods such as Kalman Filter, and approximation in the sense of finite mixture distributions, where VBDAF is approximate in the sense of KL divergence. We demonstrate VBDAF, in application of online multiple target localization.