# Multi-Information Fusion Indoor Localization Using Smartphones
## 5 Experimental Verification and Analysis
### Localization Performance  
We performed several experiments of localization performance on the global region at the two trial sites. Figure 13 shows the localization results using CHAN, PDR, improved PDR, and the proposed method at the two trial sites. These results show that the localization of the CHAN algorithm and the PDR algorithm is affected by the environment. Some abnormalities occur in the acoustic localization, and cumulative errors occur over time in the PDR algorithm. However, the improved PDR algorithm and proposed algorithm can achieve better accuracy and effectively suppress the abnormity and cumulative errors. This result is attributed to the following reasons. First, the proposed PDR method can effectively extract accurate information to estimate location. In addition, we proposed error judgment criteria to eliminate anomalous values. The proposed method thus exhibits good generality for different environments. Figure 11: **The** CDF of localization errors between our PDR and PDR algorithms 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. Figure 14 shows the localization results using CHAN, PDR, improved PDR, and the proposed method with different devices at the trial sites and demonstrates that there is equipment heterogeneity in the CHAN and PDR estimation. The proposed method can achieve comparable localization performance in the survey path primarily because the proposed method can effectively compensate for the equipment difference and suppress the errors. The proposed method thus achieves better performance with different devices compared to the other tested methods. Figure 12: The CDF of localization errors between our PDR and PDR algorithms 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. Figure 13: Localization path results among CHAN, PDR, dynamic improved PDR, and the proposed algorithm at the two scenes. (**a**) Volunteer #1 with a VivoY85a cell phone in the first scene. (**b**) Volunteer #2 with a VivoY85a cell phone in the second scene. Figure 15 shows the localization results using CHAN, PDR, improved PDR, and the proposed method with different height pedestrians at the trial sites. These results show that different heights cause different errors in the estimation algorithms. However, the proposed method achieves good accuracies with different user heights along the survey path because the proposed method can effectively compensate for the errors that different user heights create. Figure 16 demonstrates the mean localization errors between CHAN, PDR, dynamic improved PDR, and the proposed method for different devices, scenes, and step counting. These results show that the proposed method achieves comparable localization accuracy with different length paths, devices, and pedestrians. The proposed method achieves the best localization performance compared to CHAN, PDR, and dynamic improved PDR because the proposed threshold scheme suppresses outliers caused by the environment in the base-acoustic estimation and the cumulative errors caused by PDR estimation over time. Figure 14: Localization path results among CHAN, PDR, improved PDR, and the proposed algorithm using the VivoY85a and Honor60 smartphones in the first scene. (**a**) Volunteer #2 with a VivoY85a cell phone in the first scene. (**b**) Volunteer #2 with an Honor60 cell phone in the first scene. Figure 15: Localization path results among CHAN, PDR, improved PDR, and the proposed algorithm with two different height pedestrians. (**a**) Volunteer #1 (height 155 cm). (**b**) Volunteer #2 (height 180 cm).