Spatiotemporal Clustering for Aggregating Hostile Units in Cluttered Environments

We describe a novel clustering approach for aggregating mobile (typically potentially hostile) units in cluttered urban environments. The approach consists of a suite of spatiotemporal clustering algorithms that leverage the wealth of military sensor data available to provide insight into "what is strange" about a given situation, without knowing beforehand what exactly we are looking for. The algorithms perform a space and time-series analysis of sensor messages independently of any contextual or semantic information. The algorithms can, for example, detect patterns and track for spatially correlated moving units over time within the environment. The patterns thus detected trigger follow-up assessment of the newly developed situations, resulting in invocations of various doctrine-based computational models to identify higher-level situations (e.g. attack, ambush, interdiction, insurgency). We provide some experimental results analyzing the performance of the clustering algorithms