# Multi-Information Fusion Indoor Localization Using Smartphones
## 5 Experimental Verification and Analysis
### Discussion and Analysis
#### 5.2.2 Step Length Estimation  
To evaluate the proposed step length method, we performed experiments with the Scarlet, Kim, Weinberg, and proposed models. Figure 9 demonstrates the results of step length estimation at the first experimental site when the pedestrian walks at a speed of 0.6 m/step. Figure 9 shows that the proposed method achieves better performance than the Scarlet, Kim, and Weinberg models with different user heights and different devices. These results primarily occur because the proposed model can identify more accurate information to calculate the step length for each time step.  
Table 2 shows the step length estimation with the VivoY85a and Honor60 smartphones in the first scene with the various models. The step length estimation of the proposed model is more accurate than the other models. Comparing these methods, the proposed method achieves performances near the true value for different persons. Equipment heterogeneity can thus be managed effectively.  
For the second scenario, Figure 10 demonstrates the step length estimation results when the different pedestrians with VivoY85a and Honor60 smartphones walk with a speed of 0.6 m/step. These results illustrate that the proposed model achieves a higher accuracy than the other models in the second scenario. Whether there is equipment heterogeneity or different users, the proposed method achieves good performance.  
Figure 8: The peak and valley detection results with Honor60, VivoY85a, and two different height pedestrians in the second scene. (**a**) Volunteer #1 with a VivoY85a cell phone. (**b**) Volunteer #2 with a VivoY85a cell phone. (**c**) Volunteer #1 with an Honor60 cell phone. (**d**) Volunteer #2 with an Honor60 cell phone.  
Table 3 shows the step length estimation with the VivoY85a and Honor60 smartphones in the second scene with the various models. Results illustrate that the proposed model achieves a higher accuracy primarily because the proposed model can consider more information than the other models. Additionally, the adaptive computation of attention at each step allows for the prediction of more accurate pedestrian states.  
In addition, we conducted some experiments to estimate step length with different distances at the trial site. We recruited three volunteers to capture the data with the corridor totaling 15 m, 24 m, and 33 m. Five sets of data from the inertial sensors were collected every time. Table 4 shows the distance estimation and absolute errors for each volunteer, which demonstrate that errors increase with longer distances. However, our method is more accurate than the Weinberg method. The errors of the proposed model are less than  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Volunteer \#1 (m)** & **Volunteer \#2 (m)** \\ \hline [PERSON] (VivoY85a) & 0.6621 & 0.6453 \\ [PERSON] (Honor60) & 0.6190 & 0.5619 \\ [PERSON] (VivoY85a) & 0.5375 & 0.5238 \\ [PERSON] (Honor60) & 0.5418 & 0.4859 \\ [PERSON] (VivoY85a) & 0.5532 & 0.5514 \\ [PERSON] (Honor60) & 0.5608 & 0.5494 \\ Proposed method (VivoY85a) & 0.6078 & 0.5956 \\ Proposed method (Honor60) & 0.6066 & 0.5952 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Step length estimation with Honor60, VivoY85a, and two different height pedestrians in the first scene.  
Figure 9: Step length estimation results with Honor60, VivoY85a, and two different height pedestrians in the first scene. (**a**) Volunteer #1 with a VivoY85a cell phone. (**b**) Volunteer #2 with a VivoY85a cell phone. (**c**) Volunteer #1 with an Honor60 cell phone. (**d**) Volunteer #2 with an Honor60 cell phone.