# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 5 Conclusions  
Firstly, focusing on the problem of the heterogeneity of software and hardware in different mobile terminals, we propose an indoor Wi-Fi RSSI calibration algorithm based on a WOA-BPNN. We use the WOA to find the optimal initial weights and thresholds of the BPNN, which addresses the drawback of the BPNN model falling easily into the local optimum. By applying a nonlinear convergence factor \(a\), we further improve the convergence speed of the optimisation algorithm. The proposed calibration algorithm is simple to operate for handheld mobile terminals. We also compare the impact of the different structures of the BPNN model on the final calibration effect of the WOA-BPNN calibration algorithm. The experimental results show that the BPNN model with double hidden layers is more beneficial to the calibration effect of the algorithm than the BPNN model with a single hidden layer. Moreover, the test results suggest that a larger number of neurons in the hidden layer of the BPNN model do not directly lead to better calibration results by the WOA-BPNN calibration algorithm, because too many neurons in the hidden layer will lead to over-fitting in the network [11]. Secondly, to solve the problem of the low positioning accuracy of location fingerprint indoor positioning algorithms based on Wi-Fi RSSI, we propose a region-adaptive WKNN positioning algorithm based on hierarchical clustering. Finally, we further combine the WOA-BPNN calibration algorithm (where the hidden layer of the BPNN model is double layers, and the number of neurons in each layer is six) with region-adaptive WKNN position algorithm based on hierarchical clustering. Compared with the one-sigma positioning error of the different mobile terminals using the LR, SVR, BPNN and GA-BPNN calibration algorithms, the one sigma positioning error of the mobile terminals using the proposed WOA-BPNN calibration algorithm is reduced by 41%, 42%, 44% and 36% on average. In addition, the proposed WOA-BPNN calibration algorithm reduces the two-sigma positioning error of the mobile terminal by 37%, 25%, 26% and 17% on average, compared with the four other calibration algorithms. Thus, the proposed WOA-BPNN calibration algorithm can effectively reduce the indoor positioning error caused by the heterogeneity of software and hardware in different mobile terminals and improve the availability and universality of the positioning algorithm. [PERSON] and [PERSON] guided the research work, supervised and organized the paper writing; [PERSON] and [PERSON] conceived and designed the methodology and experiments; [PERSON] and [PERSON] performed the experiments, analysed the data and wrote the paper; [PERSON] and [PERSON] revised and submitted the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by the Program of the Ministry of Science and Technology, under Project application No. : S2022 QYCXH0051. Not applicable. Figure 20: Cumulative distribution function (CDF) of positioning error. **Data Availability Statement:** Not application. **Acknowledgments:** We would like to express our heartfelt thanks to anonymous reviewers and editors for their constructive comments on the paper. **Conflicts of Interest:** The authors declare no conflict of interest.