This article focuses on Group Target Tracking (GTT) to counter swarms of drones using Random Finite Sets (RFSs) and Random Matrix (RM) approaches. Tracking swarms of drones is analog to tracking extended targets that are characterized by their continuously evolving shape and composition. Extended target tracking for groups of targets finds various applications in the literature because detecting and tracking each individual target of a group is computationally demanding and unnecessary if the group itself can be modeled. Elliptic shapes offers a suitable representation for most groups, and their inference is quite inexpensive with the random matrix approach. Indeed, they are efficient when coupled with random finite sets based filters, which represents the current state of the art for Bayesian multi-target tracking. In this work, a practical implementation of a labeled Poisson/Multi-Bernoulli filter using random matrices for group target tracking is proposed. This study compares several random matrix prediction and update algorithms with and without random finite sets based filters on several dataset.