Variational Approximation Data Association Filter

We apply a variational approximation for multiple-target localization, and propose Variational Approximation Data Association Filter(VADAF) method, which minimize KL divergence between marginalized likelihood and approximated one. For multiple-target localization, we have to solve data association problem. The data association problem is that we can not associate data and targets deterministically, when data don´t have unique labels associated to targets. JPDAF is widely used for multiple-target tracking (MTT). It is extended filtering method based on Sequential Bayesian Estimation methods, such as Kalman Filter. Our method is not only based on the sequential bayes estimation, but based on variational approximation method. Our main contribution is derivation of variational approximated likelihood of targets´ states, and optimize it by minimizing KL divergence. It is more precisely than mixture likelihood of JPDAF method.