In this paper, we consider the problem of cooperatively control a formation of networked mobile robots/vehicles to optimize the relative and absolute localization performance in 1D and 2D space. A framework for active perception is presented utilizing a graphical representation of sensory information obtained from the robot network. Performance measures are proposed that capture the estimate quality of team localization. We show that these measures directly depend on the sensing graph and shape of the formation. This dependence motivates implementation of a gradient based control scheme to adapt the formation geometry in order to optimize team localization performance. This approach is illustrated through application to a cooperative target localization problem involving a small robot team. Simulation results are presented using experimentally validated noise models.