## 4 Experiment and Result Analysis
The average localization error in the proposed sensor fusion framework is lower than in conventional sensor fusion frameworks. The average localization error from the PDR+Wi-Fi fingerprint approach shows a constant average localization error for most of the experiment time. This is due to the fingerprint map approximation between online data and offline data. The PDR+Wi-Fi trilateration approach shows the worst performance compared to other sensor fusion approaches. The localization accuracy of PDR+Wi-Fi trilateration approach depends on the multipath effects. The NLOS conditions and free space path loss model in the PDR+Wi-Fi trilateration approach yields larger position errors than  
Figure 10: Average localization error from linear motion. Figure 9: Average localization error from rectangular motion. other sensor fusion approaches. The probability distribution of localization errors for sensor fusion frameworks in both experiment scenarios is shown in Figures 11 and 12. From probability distribution of the localization error plots, it is clear that the proposed sensor fusion framework is better than other methods and gives high position accuracy for indoor localization. When the experiment starts, the proposed sensor fusion framework and PDR+Wi-Fi fingerprint approach show the same localization errors. When the experiment time increases the proposed sensor fusion framework shows the highest position accuracy as compared to the other sensor fusion frameworks. At some point in the experiment, the PDR+Wi-Fi fingerprint and PDR+Wi-Fi trilateration approaches show the same localization errors. Tables 2 and 3 summarize the performance of the proposed sensor fusion framework versus conventional sensor fusion frameworks in terms of mean error, maximum error, minimum error, and standard deviation of error (STD). Figure 11: Probability distribution of the localization error for rectangular motion. (**a**) PDR+Wi-Fi trilateration. (**b**) PDR+Wi-Fi fingerprint. (**c**) Proposed Sensor Fusion Framework. From Tables 2 and 3, the proposed sensor fusion framework has a lower mean error and maximum error than conventional sensor fusion approaches. The mean error results show that the PDR+Wi-Fi trilateration and PDR+Wi-Fi fingerprint approaches have almost similar error results compared to the proposed sensor fusion approach. However, the maximum error from PDR+Wi-Fi trilateration is very high compared to the other two sensor fusion approaches. The minimum error results indicate that the PDR+Wi-Fi trilateration approach has the least minimum error compared to the other two approaches. The standard deviation of error results validates the significance of the proposed sensor fusion framework with respect to conventional sensor fusion frameworks. From all the experiment results and analysis, we conclude that the proposed sensor fusion approach significantly outperforms the proposed sensor fusion approach. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Position Estimation Systems** & **Mean Error (m)** & **Max. Error (m)** & **Min. Error (m)** & **Standard Deviation of Error (m)** \\ \hline PDR+Wi-Fi trilateration & 0.54 & 1.7 & 0.01 & 0.45 \\ \hline PDR+Wi-Fi Fingerprint & 0.50 & 1.38 & 0.5 & 0.39 \\ \hline Proposed sensor fusion framework & 0.42 & 1.17 & 0.02 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different sensor fusion frameworks for path 1 rectangular motion. Figure 12: Probability distribution of the localization error for linear motion. **(a)** PDR+Wi-Fi trilateration. **(b)** PDR+Wi-Fi fingerprint. **(c)** Proposed Sensor Fusion Framework. that of conventional sensor fusion approaches and shows significant position accuracy for indoor localization.