Improved method of bluetooth-low-energy-based location tracking using neural networks

Abstract

Indoor positioning and tracking technology perform important functions in augmented reality, smart factories, and autonomous driving. The indoor positioning method using a Bluetooth low energy (BLE) beacon has been considered challenging, owing to the deviation of the receiver signal strength indicator (RSSI) value. In this paper, we propose an indoor location tracking method by adding an algorithm to reduce differences between the actual and predicted locations of moving objects. By using synthetic data generated from actual measured values, neural networks were trained and used to predict the location of the beacon. Also, an improved tracking algorithm of moving objects was proposed by considering the angle of rotation relative to the origin. Through the simulation, it was confirmed that the improved tracking results were obtained by applying the proposed tracking algorithm to the locations predicted by neural networks.