Improved Bluetooth-Based Indoor Localization for Devices Heterogeneity Using Back- Propagation Neural Network

Abstract

Positioning accuracy can be compromised by the heterogeneity of software and hardware among different intelligent mobile devices. This is due to the fact that the heterogeneity of different devices leads to a significant difference in the received signal strength index of the same Bluetooth access point (AP) captured at the same acquisition point of the device. To address this issue, we propose to use the honey badger algorithm back-propagation neural network (HBA-BPNN) model for calibration. The aim of this study is to calibrate the received signal strength indicator (RSSI) received by Bluetooth sensors of distinct intelligent mobile terminal devices to solve software and hardware heterogeneity issues. Second, this article uses an indoor fingerprint localization algorithm based on an improved generalized regression neural network (GRNN) model and combines it with the calibration algorithm to build a better localization model. Finally, we verified the effectiveness of the HBA-BPNN calibration model for different test intelligent mobile terminal devices and then compared and analyzed the calibration algorithm proposed in this study with different calibration algorithms. The experimental comparative analysis results show that the positioning accuracy can reach 0.84 m by combining the proposed calibration algorithm with the positioning algorithm.