This paper introduces a decentralized framework for optimizing the coordination of robot networks to track multiple moving targets in applications like security and surveillance. The problem of network optimization is proven to be NP-hard, highlighting the need for efficient solutions. The proposed frame- work presents two novel decentralized coordination methods: the group-based algorithm and the bundle-based algorithm. These methods aim to achieve adaptive and conflict free target assignments, with the bundle-based algorithm providing more effective coordination and guaranteeing a 12 approximation in the worst-case scenario. Simulation results demonstrate that the proposed approaches outperform existing algorithms, achieving performance close to the optimal solution in significantly less time. Compared to EER control and PD control, the group-based assignment and control (GBAC) and the bundle-based assignment and control (BBAC) demonstrate superior performance due to their adaptive target assignments achieved through network coordination. Among the two methods, BBAC shows higher average target tracking rate (ATTR) and lower average robot traveling distance (ARTD), resulting in improved track- ing efficiency. Physical experiments using a network of ground robots tracking human targets further validate the practicality of the proposed approach in real- world scenarios.