# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
###### Abstract  
Owing to the heterogeneity of software and hardware in different types of mobile terminals, the received signal strength indication (RSSI) from the same Wi-Fi access point (AP) varies in indoor environments, which can affect the positioning accuracy of fingerprint methods. To solve this problem and consider the nonlinear characteristics of Wi-Fi signal strength propagation and attenuation, we propose a whale optimisation algorithm-back-propagation neural network (WOA-BPNN) model for indoor Wi-Fi RSSI calibration. Firstly, as the selection of the initial parameters of the BPNN model has a considerable impact on the positioning accuracy of the calibration algorithm, we use the WOA to avoid blindly selecting the parameters of the BPNN model. Then, we propose an improved nonlinear convergence factor to balance the searchability of the WOA, which can also help to optimise the calibration algorithm. Moreover, we change the structure of the BPNN model to compare its influence on the calibration effect of the WOA-BPNN calibration algorithm. Secondly, in view of the low positioning accuracy of indoor fingerprint positioning algorithms, we propose a region-adaptive weighted K-nearest neighbour positioning algorithm based on hierarchical clustering. Finally, we effectively combine the two proposed algorithms and compare the results with those of other calibration algorithms such as the linear regression (LR), support vector regression (SVR), BPNN, and genetic algorithm-BPNN (GA-BPNN) calibration algorithms. The test results show that among different mobile terminals, the proposed WOA-BPNN calibration algorithm can increase positioning accuracy (one sigma error) by 41%, 42%, 44% and 36%, on average. The indoor field tests suggest that the proposed methods can effectively reduce the indoor positioning error caused by the heterogeneous differences of software and hardware in different mobile terminals. BP neural network clustering algorithm; heterogeneity of software and hardware; indoor positioning; whale optimisation algorithm; Wi-Fi RSSI calibration 2022141 July 202214 July 20221  
solve the above problems and improve indoor positioning accuracy, calibrating the Wi-Fi RSSI values received by different mobile terminals at the same position is necessary to make the RSSI values consistent. For indoor positioning and calibration, [PERSON] et al. [3] proposed recording the signal strength collected by different mobile terminals and using linear regression (LR) model to calibrate the difference between different mobile terminals, which could help improve positioning accuracy. However, owing to the nonlinear logarithmic variation in RF signal strength with distance and the simple linear calibration method, the positioning accuracy was poor. Therefore, an increasing number of scholars are examining nonlinear indoor positioning and calibration algorithms. [PERSON] et al. [4] proposed a new algorithm that used support vector regression (SVR) to construct the nonlinear mapping relation between the RSSI and distance between tags and readers. [PERSON] et al. [5] proposed the possibility of the nonlinear calibration of Wi-Fi signals by establishing a back-propagation neural network (BPNN) model. Through repeated training, this method can obtain a relatively stable BPNN calibration model, but the improper selection of the initial parameters of a BPNN model will lead to poor calibration results. To solve this problem, [PERSON] et al. [6] proposed to further optimise the initial weights and thresholds of a BPNN model by using the selection and mutation process of a genetic algorithm (GA) to overcome the problem of the BPNN model falling easily into local optimisation. Although the above algorithms can reduce the error caused by the heterogeneity of software and hardware in different mobile terminals, several parameters must be set for a GA-BPNN model, which at present are selected empirically.