## 1 Introduction  
Recently, multiple unmanned aerial vehicles (UAVs) have received increasing attention for their accomplishments in both military and civil applications. Tracking of a moving ground target is one of the important applications of UAVs, with the aim of increasing the overall knowledge of the target's state (position, velocity, etc.) and taking proactive measures. The goal of cooperative target tracking is to control multiple UAVs to keep tracking a moving target and obtain more accurate target state estimates. In cooperative target tracking missions two or more UAVs track the same target. Under such a mission two main technical issues arise [1]: (1) Target motion estimation; each UAV has a local estimation of the target state (position and velocity). To obtain a more accurate target state estimate, it is necessary to develop global estimation strategies by fusing the local estimates from UAVs in an efficient manner; (2) Observer trajectory optimization; by developing cooperative control methods, UAVs can move in such a way as to minimize the error and uncertainty of the target state estimation. Extensive studies have been carried out on these two key issues. Target-motion estimation has been a major problem in the field of target tracking and has received great attention. In target tracking applications, sensors (such as camera, radar, and sonar, etc.) installed on the UAVs can obtain measurements, such as the relative range and azimuth of the target with respect to the position of the airborne UAV. According to the measurement data, the UAV can obtain a target state estimate which generally is suboptimal given the local information. Fortunately, the communication capabilities of the UAVs enable sending their measurement data to a fusion center (e.g., base station), which leads to a global optimal estimate. Most of existing estimation and fusion methods are based on the information filter (IF), which is the information form of the Kalman filter (KF). For the IF, the information vector and matrix are employed instead of the mean and covariance used in the standard KF to represent the Gaussian distribution [2; 3; 4]. In this way, the IF has advantages to handle sensor fusion tasks and unknown prior covariance conditions. Thus, IF is more widely used than KF in the estimation and fusion problems for multiple sensors [5; 6]. [PERSON] [7] provides a decentralized airborne data fusion method based on the IF for ground targets tracking. [PERSON] [8] presented a new information consensus filter (ICF) for distributed dynamic-state estimation, in which estimation is handled by the traditional information filter, while the communication of measurements is handled by a consensus filter. Additionally, the unscented information filter (UIF) has been proposed for distributed estimation with nonlinear dynamics. [PERSON] [9] presented an adaptive consensus-based distributed UIF for mobile sensor networks with communication delays. These studies show that the IF and its extensions can effectively solve the problem of data fusion from different sensor platforms. The observer trajectory optimization problem focuses on determining the trajectories of multiple UAVs (note, the UAVs are the observers) such that the target state estimation error is minimized. This issue is concerned with the cooperative control of the movements of multiple UAVs to guarantee the optimal observer trajectory for target motion estimation. Different earlier studies have developed observer trajectory planning approaches, including the gradient-based control law [10; 11], Lyapunov vector fields (LVF) [12; 13], and receding horizon optimization (RHO) [14; 15], among others. In References [10; 11], an optimal sensing strategy was given by minimizing the determinant of the estimation error covariance matrix, and a gradient-based control law was derived for each sensor platform to reach a local optimum. However, kinematic constraints of the platform were not considered, and this gradient-based control strategy was easily trapped in local optimal solutions.