In this paper, we improve the Partitioned Sampling (PS) scheme to better handle high-dimensional state spaces. PS can be explained in terms of conditional independences between random variables of states and observations. These can be modeled by Dynamic Bayesian Networks. We propose to exploit these networks to determine conditionally independent subspaces of the state space. This allows us to simultaneously perform propagations and corrections over smaller spaces. This results in reducing the number of necessary resampling steps and, in addition, in focusing particles into high-likelihood areas. This new methodology, called Simultaneous Partitioned Sampling, is successfully tested and validated for articulated object tracking.