## 1 Introduction
Based on this, current scholars have proposed a new tracking method that simultaneously tracks this type of target's motion state, size, and expanded shape. When portraying a group/extended target shape, the ellipse shape is widely used in target tracking as a representative and basic shape. To describe the extended shape of these targets, scholars combine techniques such as random matrices [17; 18], random hypersurfaces [19; 20], Gaussian processes [21; 22], and so on. The random matrix technique is widely used in group/extended target tracking due to its simplicity and high efficiency in tracking these targets. The aforementioned tracking frameworks can be utilized to realize the tracking of multi-group/extended targets. Regarding the second issue, distributed information fusion is challenged by unknown associations between different sensor data. Although a theoretically optimal fusion method has been proposed, it is not easy to apply in practice. [PERSON] proposed a generalized covariance intersection (GCI) for performing information fusion [23]. Several GCI-based filters have been proposed; these are also called weighted geometric averaging (WGA) within RFSs, and they include PHD-GCI [24], multi-Bernoulli (MB)-GCI [25], and LMB-GCI [26]. WGA minimizes the sum of the Kullback-Leibler divergences (KLDs) from an information-theoretic point of view, and it involves logistic opinion pooling. As another method of minimizing KLDs, weighted arithmetic averaging (WAA) also provides a solution for information fusion under conditions of unknown data relevance, and it involves linear opinion pooling [27]. Similarly, WAA-based approaches such as PHD-WAA [28], CPHD-WAA [29], and MB-WAA [30] have been proposed. When comparing these two fusion methods, WAA has an advantage in terms of low target detection performance and false and missed detection, but it has a disadvantage in terms of estimation accuracy. The fields of view (FOVs) of different sensors are inevitably inconsistent in distributed multi-target tracking. Due to the variability of the targets in the respective FOVs of different sensors, the performance of multi-target tracking is degraded when information fusion is applied directly by applying WAA or WGA; see [28; 31] for more details. As for the limited FOV problem, under the condition that the FOVs are available a priori, [PERSON] proposed the CPHD method [32], and [PERSON] proposed the state-dependent WAA (SD-WAA) fusion method [28]. Due to the problems of time-varying FOVs or unknown a priori FOV information, fusion methods based on a priori viewpoints are no longer applicable. Subsequently, [PERSON] and [PERSON] proposed the clustering WAA-PHD [28] and clustering GCI-PHD (CPHD) [33] algorithms, respectively, based on a clustering mechanism. The clustering GCI algorithm constructs predefined thresholds based on the Mahalanobis distance and clusters Gaussian mixture (GM) terms from different sensors representing different targets, while the clustering WAA algorithm clusters GM terms based on the highest a posteriori density (HPD) \(\alpha\)-confidence region [28]. The clustering results have a specific dependence on predefined thresholds. It is easy for these methods to cluster targets that do not belong to them or fail to cluster targets that belong to them. The choice of the appropriate threshold value is still an issue that is the subject of research. [PERSON] used the target assignment method to achieve information fusion in the presence of different viewpoints, and this method requires the selection of an appropriate assignment threshold distance beforehand [34]. In addition to these single-frame methods, [PERSON] utilized the optimal sub-pattern assignment (OSPA) metric and proposed the track consensus OSPA-on-OSPA (TC-OSPA\({}^{(2)}\)) track-to-track fusion algorithm, which employs multi-frame tracking results [35]. However, the real-time performance of the algorithm is partially degraded because of the use of multi-frame results.