## 1 Introduction
[PERSON] [12] introduced Lyapunov guidance vector fields in an approach that maintains a prescribed standoff radius. Additionally, [PERSON] [13] proposed a coordinated standoff tracking method for groups of moving targets using multiple UAVs. The vector field guidance approach was applied to track a group of targets for a single UAV by defining a variable standoff orbit, to be followed, which keeps all targets within the field-of-view of the UAV. However, their study neglected the accuracy of target motion estimation in the tracking process. Target motion estimation is a dynamic process and as a result, the performance of the estimation depends on the trajectory of the sensor platform. To generate observer trajectories that improve the performance of the target-motion estimation, several observer trajectory planning methods that are based on receding horizon optimizing have been proposed. For example, [PERSON] [14] presented an information theoretic approach for vehicle trajectory optimization in the 3D bearings-only target localization problem. The trace of the Fisher information matrix (FIM) was used as the quality metric for assessing the estimation performance and gave a framework for the choice of vehicle trajectories that increased the information provided by the measurements to improve the accuracy of the estimation. In Reference [15], a receding horizon control scheme for trajectory planning in target tracking was presented for the problem of cooperative persistent target tracking in an urban environment. In that study, the tracking objective was to maximize the information contribution from each UAV. In cooperative target tracking missions, each UAV must process the measurement results received from others to improve its estimate, and thus communication plays a key role in target tracking. However, most of current researches assume ideal or non-realistic communication and consider only sensing objectives. For instance, it is common to assume either perfect links or links that are perfect within a certain radius, a significant over-simplification of communication links. Wireless transmission is degraded by several factors such as path loss, shadowing, fading, and noise. Therefore, considering the impact of communication on estimation and control is an emerging area of research [16]. Reference [17] investigated how to use the UAVs to build up a communication network rapidly in a disaster environment. The deployment of the UAVs is optimized such that a line-of-sight situation is achieved as well as a minimum required signal-to-noise ratio being ensured at the receivers. However, the scenario in this literature is static, and the method is cannot be directly applied to the moving target tracking scenario that is considered in this paper. The problem of integrating communication and sensing together for information gathering tasks is treated in Reference [18]. The approach of [18] optimizes information metric over a finite horizon which accounts for the qualities of communication links between the UAVs. In the scenario, once the range is too large for direct communication to the base station, some UAVs act as communication relays to maintain connectivity and further improve performance. Therefore, the UAVs automatically establish fixed multi-hop chains to get the measurements back to the base station. In this the fixed communication topology, the formation of the UAVs is rigid [19]. The rigid formation has higher stability, because the only permissible smooth motions are translations or rotations of the entire formation. However, due to the fixed communication topology, the algorithms do not produce efficient results in the dynamic scenario of moving target tracking. From these earlier studies it is clear that the motion decisions of each UAV should consider both the information gained through its sensor as well as communication with fusion center.