# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 4 Intelligent Integration Model of Wi-Fi/BLE/QR/Mems Sensors
### Experiment Results of the 3D-WBQM Algorithm  
In this section, two adjacent floors containing the open area and corridor area were adopted as the second experimental scenario, which is described in Figure 14. Three Wi-Fi APs were deployed at the gates and the elevator entrance of the fourth floor and the BLE nodes were deployed at the corridor scene; points L, O, R, W, Z, and the QR codes were deployed at gate points K and P. The crowdsourced trajectories were collected during a period of two days and an overall number of 46 trajectories provided by the different pedestrians were selected and generated as the final crowdsourced navigation database. For the accuracy evaluation of the 3D-WBQM framework, a cross-floor experiment was conducted; the tester began with point A, proceeded across point B-Z, and finally returned to point A. The specific route was A, B, C, D, E, F, G, A, D, E, H, I, F, G, J, K, L, M, N, O, N, M, L, K, P, Q, W, X, Y, Z, Y, X, W, Q, R, S, V, U, T, S, R, Q, P, F, G, A. The estimated 2D and 3D trajectories calculated by the inertial odometry- (IO) and RUKF-based multi-source fusion frameworks using the different combinations of location sources are shown in Figure 15a,b. Figure 15 indicates that that there was a cumulative error in inertial ranging as usage time grew, especially when the pedestrians walked across floors. The proposed 3D-WBQM framework significantly reduced the cumulative errors in inertial odometry by fusing multi-source information using a universal fusion approach. A comparison of the CDF errors between the two methods is shown in Figure 16.  
Figure 13: Accuracy comparison of CED and QED. Figure 16 describes the significant improvement of 3D indoor-localization performances using the 3D-WBQM framework compared with a single inertial odometry approach. The overall estimated localization error was within 4.4 m in 75%, using the integration of IO, BLE, and QR; the overall estimated localization error was less than 3.55 m in 75% using the combination of IO, BLE, and QR and crowdsourced Wi-Fi fingerprinting; the overall estimated positioning error as within 1.66 m in 75% using the combination of IO, BLE, and QR, crowdsourced Wi-Fi fingerprinting, and Wi-Fi FTM, especially in the Wi-Fi FTM-supported indoor area, where the localization accuracy reached 1.08 m in 75%. Finally, we performed a comprehensive comparison between the proposed 3D-WBQM framework and the state-of-the-art WFS-F solution [44], which contained hybrid fingerprinting of Wi-Fi FTM and RSSI. To be fair, the same constructed crowdsourced database was adopted, and the FTM database was generated using the same crowdsourced trajectories data. In addition, the same test routes were applied to the positioning-accuracy estimation. The result of the comparison of the two algorithms is shown in Figure 17.  
Figure 14: (**a**) Walking route at 4 th floor; (**b**) walking route at 5 th floor. (A–Z indicate the ground-truth turning points of walking route, red arrows indicates the moving direction). Figure 16: Estimated CDF errors using inertial odometry and RUKF. Figure 15: (**a**) Estimated 2D trajectories using inertial odometry and RUKF; (**b**) estimated 3D trajectories using inertial odometry and RUKF. It can be seen in Figure 17 that the presented 3D-WBQM structure achieved higher positioning precision than the WFS-F approach, with the overall positioning accuracy of the two algorithms reaching 1.66 m in 75% and 2.33 m in 75%, respectively. Therefore, the proposed 3D-WBQM framework realized an autonomous and highly adaptive 3D indoor-localization performance than those of other localization approaches, providing a comprehensive solution for large-scale indoor localization. Furthermore, there are some other factors that affect the performance of the overall 3D-WBQM framework. First, the number of extracted features and the trained trajectories led to different performances of the final error-prediction model.