## 1 Introduction  
Multi-target tracking is used to estimate the number of unknown time-varying targets and corresponding target states in environments that are subject to noise, false detection, and missed detection. Tracking all targets in a region of interest using a traditional single-sensor-based multi-target tracking approach is challenging because of single sensors' limited sensing capabilities. Therefore, multi-sensor multi-target tracking methods have emerged, and they can be broadly classified into three categories according to their different information processing methods, namely, centralized [1, 2], distributed [3, 4], and hybrid. A hybrid system incorporates both centralized and distributed processing methods. Compared with a centralized framework, a distributed processing framework has the advantages of low communication limitations, high flexibility, strong robustness, and resistance to single-sensor failure. Therefore, such frameworks play an increasingly important role in multi-target tracking. There are three stages in information processing for distributed multi-target tracking: local filtering, information interaction, and distributed information fusion. Local filtering means that sensors individually perform multi-target tracking using their own measurements. Information interaction focuses on the information transmission protocol, etc. Distributed information fusion aims at obtaining unified knowledge of the targets in the region of interest, including the targets' motion states, identity attributes, etc., by fusing local filtering results with the local filtering results received from other sensors. In this study, we focus on two main issues in distributed multi-target tracking: obtaining local filtering results with good performance and merging local filtering results from different sensors. For the first issue, the existing multi-target tracking methods can be categorized into multi-hypothesis tracking [5, 6], joint probabilistic data association [7, 8], and random finite set class tracking [9; 10; 11]. The former two face the problem of complex and computationally demanding data associations, while random finite sets (RFSs) class approaches avoid the complex data association process by modeling the target states and observations as random sets. Meanwhile, as systematic tools, such approaches provide a set of strictly accurate and generalized paradigms for multi-target tracking, especially for target birth, death, false alarms, missed detection, and so on, with a solid descriptive ability, and fruitful results have been achieved in multi-target tracking. Representative multi-target tracking methods within the framework of RFSs include the following: probability hypothesis density (PHD) [12], cardinality PHD (CPHD) [13], labeled multi-Bernoulli (LMB) [14], Poisson multi-Bernoulli mixture (PMBM) [15], trajectory PMBM (TPMBM) [16], etc. Traditional multi-target tracking objects are point targets, and in tracking such targets, each target generates at most one measurement per frame. However, as the sensor resolution increases or the target-sensor distance decreases, a single target exhibits the characteristics of an extended target and generates multiple measurements; similarly, a single group likewise generates multiple measurements if multiple-point targets take on a similar form of motion as a whole and are viewed as a whole. These two types of targets no longer fulfill the point target assumption, and new tracking methods need to be sought for such targets. If a point group target is viewed as a whole for tracking without focusing on the monitoring of individuals within it, the tracking methods are similar to those used in extended target tracking. For group/expanded target tracking, more helpful information about the target cannot be grasped using only the motion state of point targets without using information such as the target's size, intention, etc.