Automatic Identification of Coordinated Targets

The increasing advances in Unmanned Systems are transforming the type of threats traditional defence systems are designed to tackle. One significant advancement is in artificial intelligence capability which allows a group of agents to perform complex collective behaviors. As consequence, providing defence systems with threat intelligence capability is becoming a necessity, where identifying collective behaviours and coordinated targets can significantly increase the effectiveness of the system. In this work, we propose a probabilistic approach to identify certain types of coordinated targets. A scoring function that utilizes accumulated state densities (ASDs) over a sliding time window is proposed to compute the likelihood of a pair of targets being coordinated. A simulation scenario including different types of coordination is used to test the performance of this proposed method.