In this paper, we study the accuracy of Cooperative Localization and Target Tracking (CLATT) in a team of mobile robots, and derive analytical upper bounds for the position uncertainty. The obtained bounds provide a description of the asymptotic positioning performance of the robots and the targets as a function of the sensor characteristics and the structure of the graph of relative position measurements. By employing an Extended Kalman Filter (EKF) formulation for data fusion, two key asymptotic results are derived. The first provides the guaranteed worst-case positioning accuracy, whereas the second determines an upper bound on the expected covariance of the estimates. We investigate the effects of jointly estimating the targets' and the robots' position, and demonstrate that it results in better accuracy for the robots' position estimates. The theoretical results are confirmed both in simulation and experimentally.