## 3 Experiments and Analysis
### Building Environment Experiment  
In order to be similar to the actual situation, we chose seven experimenters. The experimenters consisted of one commander and six soldiers. The six soldiers were divided \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensors** & **Sampling Frequency (Hz)** & **Experimenters** & **Experimental Sites** \\ \hline ICM-42688-P & 100 & & Building environment site \\ \hline IST8310 & 100 & & \\ \hline SPL06-001 & 100 & & \\ \hline SKG122S & 1 & & Forest environment site \\ \hline DW1000 & 40 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration parameters of sensors and experimental setup. Figure 4: Block diagram of DCLAEKF algorithm. Figure 5: Sensor layout. into three groups, and the same group performed the same tasks. The first experimental site was chosen inside a building. The commander was outside the building, while three groups of soldiers were inside the building. Each group of soldiers walked along a pre-measured closed trajectory, and different groups of soldiers walked along a different trajectory. The walking track of each group of soldiers is shown in Figure 6. We compared the DCLEKF algorithm and the proposed DCLAEKF algorithm, and the localization results are shown in Figure 7. The MSEs of the localization are listed in Table 2.  
Figure 7a-f shows the localization results of two kinds of algorithms. The red line is the true trajectory, the green line is the localization result of the DCLEKF algorithm, and the blue line is the localization result of the DCLAEKF algorithm. Soldiers 1 and 2 are in the same group and are shown in Figure 7a,b. Compared to the DCLEKF algorithm, the DCLAEKF algorithm suppressed the localization errors of soldiers 1 and 2 by 1.19% and 21.98%, respectively. Similarly, soldiers 3 and 4 were in the same group, as shown in Figure 7c,d. Compared to the DCLEKF algorithm, the DCLAEKF algorithm suppressed the localization errors of soldiers 3 and 4 by 5.49% and 14.29%, respectively. Soldiers 5 and 6 were in the same group, as shown in Figure 7e,f. Compared to the DCLEKF algorithm, the DCLAEKF algorithm suppressed the localization errors of soldiers 5 and 6 by 16.36% and 26.03%, respectively. Figure 8 shows the results of MSE, which were obtained by utilizing two kinds of algorithms. We compared the performance of the algorithms more clearly by analyzing the error curves. The findings of Figure 8a indicate that the DCLAEKF algorithm outperformed the DCLAEKF algorithm in terms of precision within the 0-50 s time range, whereas the performance of the DCLAEKF algorithm declined afterward. The results depicted in Figure 8b indicate that during the early stage, the DCLAEKF algorithm had comparable MSEs to DCLEKF, but during the later stage, the DCLAEKF algorithm attained lower MSEs. Similar results were obtained from Figure 8c-f. In the presence of NLOS errors, the DCLAEKF algorithm outperformed the DCLAEKF algorithm by effectively handling the NLOS errors through the adaptive estimation of range measurement noise parameters, thus reducing the final localization errors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Soldiers** & **DCLEKF** & **DCLAEKF** \\ \hline Soldier 1 & 0.69 m & 0.68 m \\ Soldier 2 & 0.70 m & 0.54 m \\ Soldier 3 & 0.63 m & 0.59 m \\ Soldier 4 & 0.49 m & 0.42 m \\ Soldier 5 & 1.21 m & 1.01 m \\ Soldier 6 & 0.67 m & 0.50 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Localization MSEs of soldiers using different algorithms in a building environment experiment. Figure 6: Reference track diagram in the building environment. Figure 7: Localization results of soldiers using different algorithms in a building 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.