# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 4 Discussion  
### Comparison with Other Positioning Algorithms  
To verify the effectiveness of the WOA-BPNN calibration algorithm proposed in this study, we utilise four positioning algorithms, namely NN, KNN, WKNN and the proposed region-adaptive WKNN, to compare the average positioning error results before and after calibration with the WOA-BPNN calibration algorithm. Figure 16 shows the comparison of the average positioning error of the different positioning algorithms. It can be seen from Figure 16 that based on the proposed WOA-BPNN calibration algorithm, the average positioning error obtained by the above four positioning algorithms after calibration is much lower than the uncalibrated average positioning error. Among the algorithms, the proposed region-adaptive WKNN algorithm obtains the best result. Thus, we can conclude that the combination of the proposed WOA-BPNN calibration algorithm and proposed region-adaptive WKNN algorithm can effectively eliminate the heterogeneous differences in software and hardware in different mobile terminals to improve indoor positioning accuracy.  
Meanwhile, to verify the effectiveness of the proposed region-adaptive WKNN algorithm based on hierarchical clustering, we also compare the different positioning errors of the four positioning algorithms based on the WOA-BPNN calibration algorithm, and the results are shown in Figures 17 and 18. From the figures, we can see that under the one-sigma positioning error, the positioning error of test mobile terminal 1 using the proposed hierarchical clustering adaptive WKNN positioning method is about 0.61 m lower  
Figure 16: Comparison of average positioning error of different positioning algorithms before and after calibration.  
Figure 15: Schematic diagram of track points of test mobile terminal 2 positioning results.  
than that of the test mobile terminal using the NN positioning algorithm. In addition, the positioning error of test mobile terminal 2 using the proposed method is about 0.76 m, 0.31 m and 0.22 m lower than that of the test mobile terminal using the NN, KNN and WKNN positioning algorithms, respectively. Under the two-sigma positioning error, the positioning error of test mobile terminal 1 is reduced by about 1.62 m, 1.33 m and 0.34 m compared with that of the test mobile terminal using NN, KNN and WKNN positioning algorithms, respectively. In addition, the positioning error of test mobile terminal 2 is reduced by about 0.76 m, 0.31 m and 0.22 m compared with that of the test mobile terminal using the NN, KNN and WKNN algorithms, respectively. Under the average positioning error, the positioning error of test mobile terminal 1 and test mobile terminal 2 using the proposed positioning method is 1.22 m and 1.31 m, respectively, and the comprehensive average error can reach 1.27 m, which is about 0.64 m, 0.24 m and 0.14 m lower than that of the three other positioning algorithms. Moreover, the average positioning accuracy of the proposed algorithm is about 33%, 16% and 10% higher than that of the NN, KNN and WKNN positioning algorithms, respectively. Thus, it can be seen that under the three types of positioning error, the proposed method is better than the other positioning algorithms.