While the tracking of multiple extended targets demands for sophisticated algorithms to handle the high complexity inherent to the task, it also requires low runtime for online execution in real-world scenarios. In this work, we derive a batch update for the recently introduced elliptical-target tracker called MEM-EKF*. The MEM-EKF* is based on the same likelihood as the well-established random matrix approach but is derived from the multiplicative error model (MEM) and uses an extended Kalman filter (EKF) to update the target state sequentially, i.e., measurement-by-measurement. Our batch variant updates the target state in a single step based on straightforward sums over all measurements and the MEM-specific pseudo-measurements. This drastically reduces the scaling constant for typical implementations and indeed we find a speedup of roughly 100x in our numerical experiments. At the same time, the estimation error which we measure using the Gaussian Wasserstein distance stays significantly below that of the random matrix approach in coordinated turn scenarios while being comparable otherwise.

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.