# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 4 Intelligent Integration Model of Wi-Fi/BLE/QR/Mems Sensors
### Accuracy Estimation of Landmark Detection and Trajectory Evaluation  
This work employed three different landmark providers, including Wi-Fi FTM stations, BLE nodes, and QR codes, which could cover large-scale indoor environments containing different scenes. A DTW-based landmark-recognition approach was developed to implement a universal strategy for providing absolute location information to inertial odometry. The raw-collected RSSI or the RTT information was transferred as a ranging distance, which was modeled as the real-time distribution of the DTW algorithm, and the DTW algorithm compared the deviations of the reference distribution and the real-time distribution and found the nearest distance between the pedestrian and the landmarks. The results of the comparison between the reference and real-time distributions are shown in Figure 9 and the results of the DTW calculation are shown in Figure 10.  
Figure 8: CDF error comparison. Figure 9: Comparison between reference distribution and real-time distribution. In addition, we conducted a comprehensive comparison of our proposed DTW approach with two different BLE- and Wi-Fi-based landmark-detection algorithms--the hybrid detection approach (HDA) [10] and critical point finding (CPF) approach [43]. The comparison results of Wi-Fi- and BLE-based landmark detection are described in Figure 11.  
Figure 11 shows that the DTW-based landmark detection approach provided better recognition accuracy in the cases of both Wi-Fi- and BLE-detection. The overall detection errors for both Wi-Fi- and BLE-based landmarks were below 0.25 m in 75% and 0.52 m in 75%, respectively. We have provided a brief summary of the three different landmark-detection approaches. The general performance comparison of three kinds of landmark-detection approaches is set out in Table 1.  
Figure 11: The Errors Comparison of Landmark Detection Algorithms. Figure 10: The DTW-calculated result. Table 1 shows that the Wi-Fi station provided the best landmark-recognition accuracy due to the precise observation of Wi-Fi FTM. The BLE node had higher detection accuracy than the QR code-based method, and both Wi-Fi and BLE were more costly when deploying landmark points. The QR landmark-detection accuracy was estimated by calculating the distance between the positions of different people, scanning the QR codes and the real locations of the QR codes based on the statistical results collected from 105 different users. In addition, other factors would also affect the performance of landmark detection, such as the channel and the coherence time of the used bands and the signal-processing mode of equipment manufacturers. In this case, we also tested the Wi-Fi APs with 5 GHz and 2.4 GHz bandwidths, respectively, and the DTW matching algorithm was applied to realize landmark detection by using a period of collected data instead of instantaneous data, which would have decreased the effects of hardware difference; more effects of the channel and the coherence time of the used bands could be further explored to improve the landmark-detection algorithm. In this work, the crowdsourced trajectories were autonomously evaluated by an enhanced trained MLP error-prediction structure. During the training phase, the data were collected from different indoor environments, including the office scene, the corridor scene, and the shopping mall scene. A total of 105 trajectories collected from different scenes were adopted as the training dataset, and after the training phase, the trained error-prediction model was applied to evaluate the localization error of each step period on the same trajectory. The final estimated accuracy of the error-prediction model was compared with the raw MLP model proposed in [8] on the same testing dataset, as described in Figure 12.  
As can be seen in Figure 12, the enhanced MLP model provided better trajectory error-prediction performance, reaching 0.65 m in 75%.