## 1 Introduction  
Multi-robot systems (MRSs) have garnered tremendous research interest in recent years [1]. Compared with a single robot, an MRS usually has greater efficiency and operational capability in accomplishing complex tasks, such as transportation [2], search and rescue [3], and mapping [4]. Among these MRS applications are the fundamental tasks of obtaining reliable localization information for the local robot and the uncooperative target using various measurements; these two processes are often referred to as collaborative self-localization (CL) [5; 6; 7; 8; 9] and distributed target tracking (DT) [10; 11; 12; 13], respectively. In the CL process, each robot measures the relative quantities with regard to neighboring cooperative robots. By cooperating with other robots, each robot is able to refine its own positioning information. In the DT process, each robot performs a measurement function on the uncooperative targets to be tracked. Then, the states of the target can be estimated cooperatively through interactions with other robots. Although the problems of CL and DT are usually solved by two separate techniques, such as in [5; 6; 7; 8; 9; 10; 11; 13; 14; 15; 16; 17],they are correlated in most practical scenarios. In the DT process, the target tracking accuracy is dependent on the localization information of the corresponding robots, as well as the relative measurements between the robot and target. The target tracking results obtained by each local robot, in turn, can be implemented to improve the localization performance of the robots. To realize MRS self-localization and target tracking simultaneously, a combined collaborative self-localization and distributed target tracking (CLAT) framework is studied in this paper. The problem of multi-robot collaborative localization has drawn significant attention in recent years. In [18], the state of the art in collaborative localization is surveyed, and the theoretical limits, algorithms, and practical challenges are discussed. As one of the fundamental challenges in CL, the application of a proper data fusion strategy to deal with the correlations between robots was studied in [5; 6; 7; 8; 9; 14; 15]. A direct approach involves the local robot treating the states of neighboring robots as fully confident variables that will lead to zero correlations between robots [5]. However, this impractical assumption of neighboring positions can lead to overconfident estimates. A more practical method to fuse the relative measurements when the correlation is unknown is the implementation of conservative correlation approximation methods, such as covariance intersection (CI) [6] or split covariance intersection (SCI) [7]. A CL approach using CI was proposed in [14]. This method is provably consistent and can handle asynchronous communication and measurement. The SCI-based approach, as studied in [7; 8], further separates the covariance into correlated and uncorrelated parts, and the latter is fused using the CI method. Despite the ability of CI-based methods to preserve the consistency of the estimates, they often have overly conservative results. Making a trade-off between estimation accuracy and the corresponding cost during the CI-based collaborative localization process was investigated using the optimal scheduling problem in [19]. Another popular method to deal with the CL problem is based on factor graphs, which are formulated on the basis of entire trajectories, such as in [15]. The correlation can be explicitly tracked in the factor-graph-based method. However, storing all of the measurements resulting from this method requires significantly more storage space than the recursive method. To address the above drawbacks, a recursive extended Kalman filter (EKF)-based CL method was proposed in [20], in which the correlation was accurately tracked in a decentralized manner. In [21], the processing and storage costs were further reduced by introducing a server that broadcasts an update message when an inter-robot relative measurement is taken.