# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 4 Intelligent Integration Model of Wi-Fi/BLE/QR/Mems Sensors
### Wi-Fi/BLE/QR-Based Landmark Detection and Uncertainty Measurement  
To autonomously generate a crowdsourced Wi-Fi fingerprinting database indoors, reference to landmark points is required to provide absolute locations for the collected crowdsourced indoor trajectories in order to further optimize the crowdsourced trajectories. Based on the signal transmission characteristics of different localization sources, in order to cover different indoor scenarios, Wi-Fi FTM-supported sites, BLE nodes, and QR codes are mixed in this work in order to provide reliable landmark-detection information according to different indoor-localization scenarios. Among them, QR codes are usually deployed near the gate, BLE nodes are usually deployed at corridors or lifts, and Wi-Fi stations are more suitable for deployment in open areas to improve landmark-detection performance. The Wi-Fi/BLE/QR-based landmark detection is depicted in Figure 3.  
Figure 2: Framework of 3D-WBQM algorithm. Figure 3 shows that the measurements acquired from the local wireless stations demonstrate regular peaks during the passage of pedestrians. In this part, a universal wireless landmark-recognition approach is presented for the accurate detection of BLE- and Wi-Fi-based landmark points. For real-world scenes, the measured BLE RSS- or the Wi-Fi FTM-based range results are affected by the multipath propagation and NLOS effects, so that the range distribution collected in real time shows fluctuating results, with differences compared to the ideal range-result distribution. In order to improve the performance of the ground-marker category and reduce the influence of environmental factors, this work developed DTW matching using the calculated distances acquired from the Wi-Fi ranging and BLE RSSI. The real-time ranging results from Wi-Fi ranging and BLE RSSI are described as:  
\[L_{\text{observed}}=L_{\text{FTM}}+d_{N}+d_{\text{random}} \tag{21}\]  
where \(L_{\text{observed}}\) is regarded as a real-time modeled Wi-Fi distance measurement which contains NLOS error \(d_{N}\) and random error \(d_{\text{random}}\), as well as the ground-truth value \(L_{\text{FTM}}\). The BLE RSSI can also be applied to distance measurements, and the conversion equation between the measured distance and the measured RSSI value is described as:  
\[L_{r}(\varsigma)=L_{0}(\varsigma_{0})-10\mu\text{lg}(\frac{\varsigma}{\varsigma _{0}})+\delta \tag{22}\]  
where \(L_{r}(\varsigma)\) indicates the measured BLE RSSI at the range \(\varsigma\) between the pedestrian and BLE node, \(\varsigma_{0}\) indicates the ground-truth distance, \(L_{0}(\varsigma_{0})\) is the reference RSSI at the known distance \(d_{0}\), \(\mu\) indicates the path loss index, and \(\delta\) represents the random error of measured RSSI. In the procedure of the pedestrian walking past a local wireless landmark, normally the measured distance set between smartphone and landmarks would generate regular peaks. Normally, there exists an ideal distribution to describe this procedure, which is generated using the constant walking-speed information and the calculated distance between the pedestrian's ideal position and the location of landmarks, as shown in Figure 3, while the collected distance set is affected by the real-world environments. To decrease the environmental effects, this paper proposed the DTW-assisted landmark recognition algorithm based on the calculated similarity results of the real-time collected measurement vector and the self-generated reference vector:  
\[ \begin{split}& DTW(\xi_{\text{refer}},\xi_{k})\\ &=Dist(e_{n},f_{m})+\min[D(e_{n-1},f_{m}),D(e_{n},f_{m-1}),D(e_{n-1 },f_{m-1})]\end{split}  \tag{23}\]  
where \(DTW(\xi_{\text{refer}},\xi_{k})\) indicates the cumulated DTW indices of the real-time collected measurement vector and the generated reference vector, \(\xi_{\text{refer}}\) and \(\xi_{k}\) represent the corre  
Figure 3: Description of Wi-Fi/BLE/QR landmark detection. sponding distributions that contain the ranging values \(e_{n}\) and \(f_{m}\).