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.

Research Progress of Wireless Positioning Methods Based on RSSI

“…The most commonly used positioning algorithm in fingerprint positioning is the KNN algorithm; fingerprint points are designed in the offline phase and the RSSI data of the AP is obtained at each fingerprint point to build a fingerprint database. The online phase matches the measurement data obtained from the point to be measured with the fingerprint library, calculates the Euclidean distance from each reference point to the point to be measured, sets the value of k, selects the k coordinates closest to MP i , and ultimately obtains the coordinates of the point to be measured through Equation ( 17) [37].…”

“…fingerprint library, calculates the Euclidean distance from each reference point to the point to be measured, sets the value of k, selects the k coordinates closest to MPi, and ultimately obtains the coordinates of the point to be measured through Equation ( 17) [37].…”

“…Hardware optimization commonly includes hardware device selection and parameter optimization, while software optimization methods are mainly related to algorithm optimization, including distance measurement method optimization, database matching method optimization, combination optimization methods, etc. to be measured, sets the value of k, selects the k coordinates closest to MPi, and ultimately obtains the coordinates of the point to be measured through Equation ( 17) [37].…”