## 4 System Analysis and Experiment
### Experiment and Analysis

Table 2: Accuracy comparisons of each method under _static_ NLOS situations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Time Each Receiving Process Is Completed** & **Accuracy** \\ \hline SVM (RBF as kernel function) & 1 & 56.4\% \\ SVM (Linear kernel function) & 1 & 55.5\% \\ SVM (Polynomial kernel function) & 1 & 44.1\% \\ SVM (Sigmoid as kernel function) & 1 & 49.8\% \\ Original logistic regression & 1 & 37.1\% \\ Original DT & 1 & 59.8\% \\ Method in [22] & 1 & 72.0\% \\ DT + CTGSA \(-\) AFSA & 1 & 66.7\% \\ SVM (RBF as kernel function) & 2 & 52.4\% \\ SVM (Linear kernel function) & 2 & 33.3\% \\ SVM (Polynomial kernel function) & 2 & 66.7\% \\ SVM (Sigmoid as kernel function) & 2 & 40.6\% \\ Original logistic regression & 2 & 45.9\% \\ Original DT & 2 & 85.2\% \\ Method in [22; 21] & 2 & 81.6\% \\ _Method in this manuscript_ & 2 & **83.3\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparisons of each method under _dynamic_ NLOS situations. since the action rules of Pattern 1 and Pattern 2 may have coincided with the NLOS coping mechanism in the system for some reason, the results of Pattern 1 and Pattern 2 were similar to those in the static NLOS scenario. However, as for Pattern 3 and Pattern 4, the results were significantly different from those in the static NLOS scenarios due to factors such as the coincidence degree of random mechanisms and rules of actions. Therefore, the system needs to select an appropriate way to produce its training data set according to the situations in the NLOS scenario during deployment, which will enable the system to play a better role. Indoor sensor networks or cognitive IoT networks are ready to obtain the users' locations, and then they will provide the users with better locations. Finally, the networks will benefit if the users are willing to obey the notifications from this system. In our experiment, different models of smartphones (e.g., XiaoMi 10 from XiaoMi Inc., Wuhan City, China. Huawei Honor 3, 4, 5, etc. from Huawei Inc. Shenzhen City, China) were adopted as the core parts of the main anchor. After determining the fixed coordinates to calculate the error accuracy, it was necessary to define the positive direction of the X axis, the positive direction of the Y axis, and the position of the O point in this site. The  
Figure 9: NLOS identification accuracy based on different methods. (**a**) Single receiving accuracy comparisons of four dynamic NLOS patterns. ; (**b**) dual-receiving accuracy comparisons of four dynamic NLOS patterns. NTS-334R electronic total station could be used to complete the calibration. During the measurements, all 12 fixed coordinates were considered to calibrate the position. These coordinates to be measured were (1, 1), (1, 1.5), (1, 2), (1, 2.5), (1.5, 2.5), (2, 2.5), (2.5, 2.5), (2.5, 2), (2.5, 1.5), (2, 1) and (1.5,1) respectively. The four dual-receiving acoustic anchors were respectively placed at (0, 0), (0, 4), (4, 4), and (4, 0). Enough distance was required to avoid reverberations or other negative interactions among the acoustic anchors, which needed to be reconsidered before deployment. The reference anchor was fixed at (1.5, 1.5), with a height of 1.5 m from the ground. The APP at the main anchor controlled the whole localization period. The NLOS identification algorithm proposed in this paper was implemented on a PC platform, which carried out vital information transfer and instruction storage commands. The NLOS acoustic signal coping mechanism in [22] was introduced to complete the process of all indoor localization systems. Some periods may have also contained dynamic NLOS situations. Meanwhile, to expand the application scope of the reference anchors effectively, we chose a VT-888 retractable stainless-steel bracket (video flexible tripod), which was often used for supporting KinectV2 in the past. We used it for supporting the reference anchor, as shown in Figure 10a below. For each coordinate, experiments were conducted 20 times.