# Sound Source Localization Indoors Based on Two-Level Reference Points Matching
## 4 Experimental Results
### Complex Scene  
According to the analysis of the computational complexity in the online location process based on clustering division, the more sub-databases divided into the offline stage, the higher the positioning efficiency in the online process. However, excessive partitioning of the location database may separate the real adjacent RPs of the same target point into different sub-databases, thus affecting the results in the RPs mismatch and reducing the positioning accuracy. The Fuzzy c-means method is used to analyze the positioning results of different clustering number positions. The partitioning results of clustering numbers ranging from 1 to 6 are shown in Figure 8.  
Figure 8: The RP partition results of the Fuzzy c-means clustering method in a complex scene.  
Figure 7: Adjacent RP searching results and position estimation results of the ninth test point based on two RP matching methods.  
When the clustering number is set as \(c=1\), 2, 3 and 4, RPs of the same sub-database are tightly gathered in the connected areas. At the same time, the number of RPs in different sub-databases are basically the same. However, as the partition count increases to 5, as Figure 8e shows, 1 outlier appears in C2 (15 red points), 1 outlier appears in C4 (12 turquoise points) and C3 (8 blue points) is scattered and contains significantly fewer RPs at the same time. When the partition number increased to 6, as Figure 8f shows, 2 outliers appear in C2 (12 red points), 1 outlier appears in C4 (10 turquoise points) and, on the whole, the partition results are obviously imbalanced.  
The WKNN position estimation algorithm is adopted for the localization test. As shown in Figure 9, the mean error and maximum error vary with the changes in the number of clusters. Compared with the traditional SSL based on global linear RP matching, the positioning accuracy of SSL based on the clustering analysis is slightly improved when the clustering number is 2, 3 or 4. However, when the clustering number increased to 5, the positioning results began to deteriorate significantly,and the average error exceeded 0.1850 m, while the maximum error reached 0.7950 m. At the same time, 72.22% of test points cannot meet the positioning accuracy requirement of 0.2000 m.  
As Table 2 shows, there is a comparison of the traditional case without a partition and four sub-databases cases based on clustering analysis. Where \(MQ_{a}\) means the average matching quantity, \(MT_{a}\) means the average matching time, \(E_{a}\) means the average error and \(E_{m}\) means the maximum error. The \(MQ_{a}\) is reduced by 74.1% through database partition based on clustering analysis, which results in the according reduction of \(MT_{a}\) in SSL based on the two-level RP matching method. In the positioning accuracy comparison, compared with the traditional linear matching method, the \(E_{a}\) and \(E_{m}\) of the two-level matching method based on database partitioning are improved by 13.18% and reduced by 8.47%, respectively. The positioning accuracy between the two RPs matching methods is almost the same.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Matching** & \(MQ_{a}\)**/Times** & \(MT_{a}\)**/s** & \(E_{a}\)**/m** & \(E_{m}\)**/m** \\ \hline No partition & 64 & 0.0232 & 0.6402 & 0.1203 \\
4 sub-databases & 16.6 & 0.0052 & 0.5558 & 0.1305 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the localization results between the traditional RPs matching method and the novel method based on clustering analysis.  
Figure 9: The mean error and maximum error of location estimation by different RP partition numbers.