## 4 Experiment Results
### Accuracy Estimation of Signal Accuracy Indicators  
This work proposes DW-KNN for improving the accuracy of Wi-Fi fingerprinting matching and applies WAI for evaluating the performance of Wi-Fi matched locations. In this work, the performance of DW-KNN is compared with three existing matching algorithms, the classical WKNN, RWKNN [18], and SAWKNN [28], and a hybrid dataset provided by IPIN-2018 [29] and IPIN-2020 [30] competitions is applied for comparison purposes. The matching results are described in Figure 5:  
Figure 5 shows that the proposed DW-KNN has a much better matching performance using the same database, and the achieved average matching accuracy reaches 2.96 m, compared with WKNN, RWKNN, and SAWKNN with 4.21 m, 3.87 m, and 3.96 m, respectively.  
In addition, this work proposes the GRU model-based Wi-Fi fingerprinting error indicator to provide error reference in the final fusion phase. The state-of-art MLP network [5] is applied for comparison purposes using the public dataset described above. The evaluation performance of the two different error prediction approaches is compared in Figure 6.  
Figure 6 indicates that the proposed GRU model proves better error prediction performance compared with the existing MLP model, and the estimated prediction errors of the two models are 1.17 m in 75% and 1.99 m in 75%, respectively.  
In addition, this work proposes the ADE to compensate for the acoustic drift error for more accurate acoustic TOA ranging results. To estimate the accuracy of ADE, the SLAM system is applied to provide the ground-truth ranging distance. An additional hardware module is connected to the smartphone for synchronization between the smartphone and acoustic stations using a 433 MHz wireless signal, which is shown in Figure 7.  
This work applies ADE under the light-of-sight (LOS) condition to compensate for the drift error of acoustic ranging, which effectively improves the accuracy of acoustic ranging by compensating for the motion-related drift. In this work, a LOS contained environment is selected to test the performance of the proposed ADE, which is described in Figure 8, and the walking route of the testers is A-K, H, I, F, G, D, E, B, A. The state-of-art acoustic/dropper positioning (ADP) method [19] is applied for comparison, and the trajectory comparison is described in Figure 9:  
Figure 9 indicates that in the developed DMDD algorithm exists cumulative error, which increases with the time period. The AD- based acoustic and MEMS sensor integration algorithm significantly decrease the positioning error, and is proven to have a better performance that the ADP approach. The positioning errors of three structures are compared in Table 2.  
Figure 6: Prediction errors comparison of MLP and GRU.  
Figure 8: Test Route in Office Scene.  
Figure 7: Synchronization between the smartphone and acoustic stations.  
Figure 9: Test route in the office scene.  
Table 2 indicates that the RMSEs of the proposed DMDD, ADP, and ADE reach 0.73 m, 0.49 m, and 0.38 m in office environment, respectively. Compared with the other two algorithms, the proposed ADE effectively improves the positioning accuracy by 47.9% and 22.4%.