# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 3 Experiments and Results
### Fusion-Positioning Experiment  
In this section, we analyzed the mean positioning accuracy and root mean square error (RMSE) of the pedestrian dead-reckoning (PDR) method, geomagnetic positioning, fusion positioning based on the classic particle filter (PF) which has no gene mutation and particle set reconstruction, and the proposed fusion method. The computational cost (CC) obtained by calculating the average positioning time of different methods is also counted for a comparison. As shown in Table 4, compared with the PDR method, geomagnetic positioning and fusion positioning by using PF, the mean positioning error of the proposed method is 1.72 m and the RMSE is 1.89 m, respectively. Moreover, 80% of the test points have the positioning error within 2.45 m. But under the same confidence level, the positioning accuracy of geomagnetic positioning, the PDR method and PF algorithm are only 4.51 m, 4.80 m and 3.22 m, respectively. In terms of the computational cost, we can find that our proposed algorithm can give positions within 0.3 s. This is slightly larger than PF, but it is still low because the size of the particle set is just 100. Table 4 shows the positioning accuracy and stability of our proposed algorithm are better improved and the computational cost is low. Moreover, the points positioning errors and the cumulative distribution of positioning errors are presented in Figure 14. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Min/m** & **Max/m** & **Mean/m** & **RMSE/m** & \begin{tabular}{c} **50\%** \\ **Error/m** \\ \end{tabular} & \begin{tabular}{c} **80\%** \\ **Error/m** \\ \end{tabular} & \
\begin{tabular}{c} **80\%** \\ **Error/m** \\ \end{tabular} &
\begin{tabular}{c} **CC/s** \\ **CC/s** \\ \end{tabular} \\ \hline PDR & 0.12 & 6.29 & 3.14 & 3.43 & 2.54 & 4.80 & 0.004 \\ Geomagnetic & 0.04 & 12.07 & 2.98 & 4.01 & 2.56 & 4.51 & 0.252 \\ PF & 0.10 & 4.83 & 1.96 & 2.28 & 1.84 & 3.22 & 0.285 \\ Proposed & 0.13 & 3.42 & 1.72 & 1.89 & 1.75 & 2.45 & 0.296 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Positioning error and computational cost (CC) table of different methods. Figure 13: Heading angles comparison by using different methods (small angles added 360\({}^{\circ}\)). (**a**) Uncorrected gyroscope VS MCF; (**b**) Electronic compass VS MCFFrom Figure 14a we can find that the positioning error of all the test points using the proposed fusion-positioning method are within 4 m and more than 70% of test points have the positioning error  
Figure 14: Positioning cumulative error curve and point error curves of different methods. (**a**) Positioning cumulative error curves; (**b**) points error curves of PDR, geomagnetic positioning and the proposed method; (**c**) points error curves of fusion positioning based on PF and the proposed method. better than 2.5 m. These are much better than the other three positioning methods. In terms of the points positioning errors, as shown in Figure 14b,c, the positioning error of the PDR method gradually accumulates with time and the error of final point accumulates to more than 6 m. Geomagnetic positioning and the fusion positioning method based on PF are both unstable and several large positioning errors appear in the error curves. Conversely, our proposed fusion-positioning method has neither a large positioning error nor the error accumulation phenomenon. The positioning accuracy and stability are improved. Based on the above error analysis, we have concluded that our proposed algorithm has better positioning performance. Figure 15 shows the positioning trajectories of different methods, the PDR method can provide more reliable positioning results at first, but the trajectory begins to shift after the first turn. What is worse, the positioning trajectory directly passes through the room after the second turn, which seriously deviates from the actual path.