## 1 Introduction
However, in this method, when a relative measurement is taken between two robots, communication involves all robots rather than just the two in the relative measurement, and this significantly increases the communication burden. Another recursive EKF-based CL method was proposed in [9]. This method efficiently approximates the correlation and only stores the current measurement. When the relative measurement is taken, only the communication between the two robots is required. The distributed tracking problem has also been extensively studied [22]. Early-stage algorithms that have been proposed to solve this problem can be roughly split into two categories: consensus-based algorithms [16] and diffusion-based algorithms [10]. The former category, in general, requires multiple communication iterations during each sampling time interval and hence could lead to a heavy communication burden. To reduce the communication bandwidth, a distributed Kalman filter with event-triggered communication was proposed in [23], and the stability is guaranteed. The latter category does not have such drawbacks, but it may require local joint detectabilities at every single agent, and such a requirement might not be satisfied in a general multi-robot target tracking scenario. A more practical DT approach called distributed hybrid information fusion (DHIF) [11] is able to guarantee stability and is asymptotically unbiased with very mild sufficient conditions. To further solve the distributed tracking problem with a nonlinear process and sensing models, an EKF-based paradigm was proposed in [24], and the stability was analyzed in [25]. Also, the unscented transformation-based approach, which has been regarded as a superior alternative to the EKF when the systems are highly nonlinear, was integrated with the DT process in [26]. However, both the EKF and unscented Kalman filter (UKF) mentionedabove are consensus-based and hence may generally result in a heavy communication burden. Recently, the aforementioned DHIF was extended to a nonlinear scenario using the DT approach in [12], and the stochastic stability was analytically studied. Besides the methodology research and theoretical analyses above, the performance of different fusion strategies in terms of their communication rate, information type, and memory size were compared in [27]. The CLAT framework has gained attention in recent years. There are two main kinds of methods: batch and recursive methods. The batch method estimates the entire state trajectory on the basis of all measurements and motion information up to the present, and the recursive method uses only the current measurement and control information. The batch-based method is supposed to outperform the recursive method but at the cost of significantly larger computation and storage requirements and, if in a distributed fashion, communication requirements. One batch-based method was proposed in [28]. By introducing a factor graph that contained robot and target nodes and relative measurements, the problem was formulated as a least-square minimization problem and was solved with sparse optimization methods. Another batch-based method was presented in [29], where the CLAT problem was formulated as a maximum a posteriori estimation problem, and the unscented transformation (UT) technique was implemented to better characterize the nonlinear process. Furthermore, the observability condition was extensively studied. For an MRS with limited computation and storage capacity, the recursive method is often preferred. A recursive-filter-based CLAT was studied in [30], and the error bounds are theoretically provided. Nevertheless, the results in [30] are based on a specially designed measurement graph so that the correlation can be tracked properly. A recursive Bayesian method was proposed in [31] to perform the CLAT in a distributed sequential fashion; however, this method needs synchronous communication at each time instance and will therefore add a significant communication burden.