In this paper, the commonly used filtering technique occupancy grid mapping for static environments is extended for dynamic environments. The proposed method is able to estimate velocities indirectly. We apply a distribution model of the respective state variable to estimate the cell dynamics by means of prediction and update cycle, as known by standard tracking filters. Therefore, we present a straight forward derivation of the prediction and update rule. Furthermore, we validate our approach by simple one dimensional simulations, and show how it can be extended into a two dimensional world, including the resulting consequences, e.g. in terms of memory requirements.