## 4 Experiment Results
### Experiment Results of the H-IPS Framework  
The aim of the proposed H-IPS framework is to realize universal indoor positioning by combining the advantages of Wi-Fi, acoustic, and MEMS sensors, and also to maintain the localization precision under NLOS conditions. In this work, a comprehensive and large-scale indoor environment is applied for the estimation of H-IPS, which is described in Figure 8, in which the acoustic covered area is described in Figure 10.  
In the above section, we tested the performance of ADE in the case of the LOS condition. In this section, a NLOS contained environment is applied for the evaluation of the proposed acoustic NLOS detection algorithm. In the acoustic covered area, adjacent rooms are separated by a wall; thus, the NLOS error is unavoidable in selected environments. The tester walks alongside the adjacent offices and the real-time received acoustic ranging results are affected by the additional NLOS bias, which needs to be detected and evaluated. The performance comparison between [19] and the proposed ADE and NLOS detector algorithm is described in Figure 11 and Table 3:  
It can be seen in Table 3 that the proposed ADE and NLOS detector realizes a much better error compensation performance, and the estimated RMSE reaches 0.26 m, the maximum positioning error is lower than 0.82 m.  
Finally, a large-scaled environment containing the corridor scene and office scene is applied to realize Wi-Fi fingerprinting and acoustic positioning at the same time. We also make a comprehensive comparison of the proposed DMDD, Wi-Fi, and built-in sensors fusion structure, and the final H-IPS. The test route of pedestrians is described in Figure 8, the trajectories estimated by different approaches are shown in Figure 10, and the accuracy comparison is shown in Figure 12:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **RMSE** & **Maximum** \\ \hline DMDD & 0.73 m & 1.32 m \\ ADP & 0.49 m & 1.06 m \\ ADE & 0.38 m & 0.91 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different algorithms under the LOS scene.  
Figure 10: Experimental site.  
As shown in Figures 12 and 13, the DMDD approach alone is still susceptible to cumulative errors when used over a prolonged time period. The accuracy of DMDD positioning reaches only 6.44 m in 75% of trials. However, our proposed fusion structure that combines Wi-Fi and MEMS sensors effectively decreases the cumulative error of DMDD and realizes a positioning accuracy within 2.37 m in 75% of trials.  
By further integrating different location sources with the adaptive unscented Kalman filter, the H-IPS achieves even higher accuracy levels, with an average precision of 2.01 m in 75% of trials. This result confirms the effectiveness of the proposed system in combining multiple sources of data for accurate indoor localization.  
Notably, our experimental results also demonstrate that meter-level positioning precision can be achieved in acoustic signal cover areas with NLOS conditions. This is a significant achievement as it allows for highly accurate indoor positioning, even in challenging environments where traditional approaches may fail.  
Figure 11: Comparison between different compensation approaches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **RMSE** & **Maximum** \\ \hline ADE/NLOS & 0.26 m & 0.82 m \\ ADE & 0.67 m & 1.41 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different algorithms under the NLOS scene.  
Figure 12: Trajectory comparison of different combinations.  
Overall, our findings highlight the potential of the proposed H-IPS for achieving reliable and robust indoor localization. By leveraging multiple sources of data and innovative algorithms, the H-IPS offers a promising solution for smart city applications that require high-precision indoor positioning. The results of this study contribute to advancing the field of indoor localization and provide valuable insights for future research and development in this area.