## 4 System Analysis and Experiment
### Experiment and Analysis  
All of the experimental situations can be divided into two parts: the LOS scenario and the NLOS scenario. As for the NLOS scenario, we were aiming at a variety of NLOS scenarios, including the static scenario and the dynamic scenario. For static NLOS situations, the transmission path was blocked by a rigid body object (plastic box, paper box, wardrobe, etc.) whose size was not large enough to occlude multiple acoustic anchors at the same time. As for the first batch, the total number of static NLOS acoustic signals was 2000 in this situation. In contrast, 2000 LOS acoustic signals were used. It was vital to consider the possibility of the NLOS scenarios with a short duration; these samples were randomly placed in the set. So, different from the samples in [20,22], the new data set was used to complete the experiment. As for the second batch, 3600 NLOS dynamic acoustic signals were included. The computer used was a Lenovo R480 from Lenovo Inc., Beijing City, China. For dynamic NLOS situations, these communication paths were blocked by our volunteers with four different patterns. We believe that these man-made and flexible NLOS situations turned out to be useful. For these dynamic situations, all of the volunteers helped us obtain 3600 dynamic NLOS signals. Single receiving structure and dual-receiving structure were also compared with each classification algorithm. Compared with the static NLOS situations, the results are showed in Table 2 (_italic_ font indicates the method used in this paper). As for the dynamic NLOS situations, the accuracy results derived from four different patterns were averaged and added to facilitate the function. The results are showed in Table 3.  
Figure 8: Indoor localization system working time axis in one localization loop. So, it is clear that the dual-receiving structure gave the system better performance in NLOS situations. Additionally, it was vital to compare the NLOS acoustic signal identification methods in every four patterns towards the dynamic NLOS situations. For the sake of convenience, the methods mentioned above were divided into single receiving and dual-receiving parts, and the numbers [1]-[16] were used in turn. As for the single receiving part, all of the methods' accuracies were as follows, in detail: The following conclusions were drawn based on the Figure 9:  
1. In the complex and flexible NLOS scenarios, the dual-receiving acoustic anchors and the adaptive acoustic signal receiving and transmitting processes improved the NLOS acoustic signal classification performance in the system significantly. Among them, the dual-receiving structure in Pattern 1 and Pattern 2 yielded no significant improvement compared with the original version, while the dual-receiving structure in Pattern 3 (running state) yielded a 15% increase in NLOS acoustic signal identification accuracy compared with the original version. Pattern 4 also showed an increase of 4-13%. It truly brings some equipment complexity, but it is considered worthwhile. 2. Compared with the identification accuracy in the static scenario, the dynamic NLOS scenarios had many differences among all methods mentioned above. Among them, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Time Each Receiving Process Is Completed** & **Accuracy** \\ \hline SVM (RBF as kernel function) & 1 & 81.7\% \\ SVM (Linear kernel function) & 1 & 77.9\% \\ SVM (Polynomial kernel function) & 1 & 70.1\% \\ SVM (Sigmoid as kernel function) & 1 & 67.8\% \\ Original logistic regression & 1 & 58.5\% \\ Original DT & 1 & 79.8\% \\ Method in [22] & 1 & 85.8\% \\ DT + CTGSA \(-\) AFSA & 1 & 61.0\% \\ SVM (RBF as kernel function) & 2 & 77.5\% \\ SVM (Linear kernel function) & 2 & 78.6\% \\ SVM (Polynomial kernel function) & 2 & 68.6\% \\ SVM (Sigmoid as kernel function) & 2 & 80.6\% \\ Original logistic regression & 2 & 65.1\% \\ Original DT & 2 & 85.0\% \\ Method in [22; 21] & 2 & 90.9\% \\ _Method in this manuscript_ & 2 & **92.8\%** \\ \hline \hline \end{tabular}
\end{table}