## 3 Experiments and Analysis
### Forest Environment Experiment  
The second experimental site was selected in the forest land and is shown in Figure 9. The groups of experimenters are the same as in the previous experiment. The soldiers were under dense trees, and the commander was in the open sky. Each group of soldiers walked along a pre-measured closed trajectory, and different groups of soldiers walked along different trajectories. The walking tracks of each group of soldiers were L-shaped areas, and the length and width were 35 \(\times\) 6 m, 20 \(\times\) 6 m, and 10 \(\times\) 6 m, respectively. The experimental results are shown in Figure 10, and the MSEs of localization are listed in Table 3.  
Soldiers 1 and 2 were in the same group, and they walked along the 35 \(\times\) 6 m L-shaped trajectory shown in Figure 10a,b. Compared to the DCLEKF algorithm, the DCLAEKF algorithm suppressed the localization errors of soldiers 1 and 2 by 20.46% and 19.69%, respectively. Similarly, soldiers 3 and 4 were in the same group, and they walked along the 20 \(\times\) 6 m L-shaped trajectory shown in Figure 10c,d. Compared to the DCLEKF algorithm, the DCLAEKF algorithm suppressed the localization errors of soldiers 3 and 4 by 69.55% and 69.32%, respectively. Soldiers 5 and 6 were in the same group, and they walked along the 10 \(\times\) 6 m L-shaped trajectory shown in Figure 10e,f. Compared to the DCLEKF algorithm, the DCLAEKF algorithm suppressed the localization errors of soldiers 5 and 6 by 41.09% and 61.96%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Soldiers** & **DCLEKF** & **DCLAEKF** \\ \hline Soldier 1 & 2.62 m & 2.09 m \\ Soldier 2 & 2.33 m & 1.87 m \\ Soldier 3 & 3.38 m & 1.03 m \\ Soldier 4 & 2.65 m & 0.81 m \\ Soldier 5 & 1.60 m & 0.94 m \\ Soldier 6 & 2.10 m & 0.80 m \\ \hline \end{tabular}
\end{table}
Table 3: Localization MSEs of soldiers using different algorithms in a forest environment experiment.  
Figure 9: Reference track diagram in the forest environment.  
Figure 11 shows the results of MSE, which were obtained by utilizing two kinds of algorithms. Figure 11a indicates that the DCLAEKF algorithm outperformed the DCLEKF algorithm with smaller errors for the majority of the time. Nevertheless, between 15-35 s, the DCLAEKF algorithm exhibited a larger error than that of the DCLEKF algorithm. Furthermore, the DCLAEKF algorithm displayed a superior performance to the DCLEKF algorithm in terms of both maximum error and error stability. Similar results were obtained  
Figure 10: Localization results of soldiers using different algorithms in a forest environment experiment. (**a**) Localization results of soldier 1; (**b**) Localization results of soldier 2; (**c**) Localization results of soldier 3; (**d**) Localization results of soldier 4; (**e**) Localization results of soldier 5; (**f**) Localization results of soldier 6.  
from Figure 11b,f. Due to the existence of NLOS errors in UWB ranging, the DCLEKF algorithm experienced sudden spikes in localization errors when NLOS occurred. On the contrary, the DCLAEKF algorithm effectively reduced the impact of NLOS errors on localization accuracy through an adaptive estimation of the range measurement noise. For example, during the time interval of 40-60 s in Figure 11b, the localization errors of the DCLAEKF algorithm were significantly smaller than that of the DCLEKF algorithm. The MSEs in Table 3 indicate that the DCLAEKF algorithm achieved a better localization accuracy than the DCLEKF algorithm. The experimental results indicate that the proposed algorithm was suitable for the cooperative localization of firefighters in the forest environment.  
Figure 11: MSE curves of soldiers in a forest environment experiment. (**a**) MSE of soldier 1; (**b**) MSE of soldier 2; (**c**) MSE of soldier 3; (**d**) MSE of soldier 4; (**e**) MSE of soldier 5; (**f**) MSE of soldier 6.