Recently, research on multi-robot networks has attracted increasing attentiondue to their higher efficiency and robustness over single-robot systems. This work focuses on the control of multi-robot networks for tracking multiple human targets, which has promising applications in security and surveillance. Existing literature has shown that the information gain can guide sensors to make informative measurements. With this inspiration, an information gain based control algorithm was developed to optimize the tracking performance of multirobot networks. The proposed control algorithm considered target tracking, target exploration, and collision avoidance. In addition, this work validated the network control through physical experiments involving Unmanned Ground Vehicles (UGVs) and real human targets, for which four online sensing algorithms including UGV localization, target detection, target localization, and target classification were implemented.