## 4 Simulation Results
In the simulation, the sensor detection probability \(P_{d}=0.99\), the measurement noise intensity \(\sigma_{v}=0.1\), and the clutter number \(\lambda_{c}\) were set to \(5,10\), and \(15\), respectively. The simulation results are shown below. In Figure 4a, it can be seen that without fusion, the MGOSPA deviation obtained with the sensors independently completing the target tracking was significant. The trend of the curves in this figure is consistent with the trend of the corresponding curves in Figure 4b, which indicates that the MGOSPA error was mainly affected by the results of the CE tracking of the individual sensors. The results of the CE, which represents the difference between the number of targets tracked by the sensors and the number of real targets, show that all of the individual sensors had different degrees, which was related to the limited FOVs of the sensors, i.e., a single sensor could not observe all targets. It can be seen in Figure 4c that, after fusion, the three methods significantly reduced the MGOSPA error compared with that of the single sensors, and the overall deviation of the CE corresponding to each method was reduced, indicating that fusion tracking can be used to solve distributed tracking with limited FOVs. In addition, comparing the three fusion methods, the proposed method was at the minimum overall in terms of both MGOSPA and CE, indicating that the proposed method is superior. In Figure 5a, it can be seen that the single sensor still obtained extensive MGOSPA results, which was still because the limited view could not be used to track all the targets. After fusion, the three fusion methods had a significant decrease compared with the single sensor (see Figure 5c). However, the WAA and WGA methods still had relatively large MGOSPA and CE results, which was related to the clutter number. With the increase in the clutter number, WGA and WAA were prone to producing erroneous fusion results, while the proposed method was less affected by the clutter number than these two, and it was slightly improved compared with that when \(\lambda_{c}=5\). The time-averaged results of these one hundred Monte Carlo simulation experiments are shown in Table 1. According to Table 1, the tracking performance of the WGA and WAA methods strongly depended on the Bernoulli-Bernoulli pairwise fusion threshold, and different pairwise fusion thresholds resulted in different tracking performances in the end. This phenomenon is crucial in practical applications, and there is still no guidance for the selection of the threshold value in the literature. In contrast, the proposed method had less of a dependence on the threshold, and the tracking performance in the presence of different thresholds was relatively close, so it is not necessary to know the threshold in advance in practical applications, thus improving the algorithm's scope of applicability. \[\gamma_{k+1} =\gamma_{k}, \tag{49}\]0 Tracking results for \(\lambda_{c}=5\), \(T_{h}=200\). (**a**) Individual sensors’ MGOSPA error without fusion. (**b**) Individual sensors’ CE results without fusion. (**c**) MGOSPA results with fusion. (**d**) CE results with fusion. Simulation 2 was conducted to investigate the effects of different detection probabilities on the algorithm's tracking performance. In this experiment, the clutter number \(\lambda_{c}=5\), the measurement noise intensity \(\sigma_{v}=0.1\), and the detection probability \(P_{d}\) were set to 0.7, 0.8, and 0.9, respectively. Table 2 shows the time-averaged Monte Carlo results obtained from these fusion approaches for one hundred trials. Overall, the tracking performance