In tracking and sensor data fusion applications, the full information on kinematic object properties accumulated over a certain discrete time window up to the present time is contained in the conditional joint probability density function of the kinematic state vectors referring to each time step in this window. This density is conditioned by the time series of all sensor data collected at the present time and has accordingly been called an accumulated state density (ASD). ASDs provide a unified treatment of filtering and retrodiction insofar as by marginalizing them appropriately, the standard filtering and retrodiction densities are obtained. In addition, ASDs fully describe the posterior correlations between the states at different instants of time. Therefore, the closed-form solution of ASDs are directly connected to many real-world problems. This article presents an overview of the applications such as out-of-sequence processing, smoothing, distributed filtering, and batch processing.