Accumulated State Densities Filter for Better Separability of Group-Targets

In some defence applications, it is required to identify targets separated by a certain distance as group-targets. This allows the system to use a suitable tracking and mitigation strategy for a group different from what is used for a point-target. A natural choice to identify a group of this type is the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The DBSCAN algorithm uses the available track information to identify the groups/clusters. Information on these tracks are, in the vast majority of tracking systems, based on the Kalman filter estimate. In this work, we present a scenario where the out-group target is inseparable from the group-target using a Kalman filter. Thereafter, we show that the separability could be significantly improved using the estimates of the joint probability density of the kinematic target states accumulated over a certain time window, up to the present time, given the time series of all sensor data. These densities are known as Accumulated State Densities (ASDs).