Improved indoor geomagnetic field fingerprinting for smartwatch localization using deep learning

Abstract

Geomagnetic field fingerprinting has attracted researchers in recent years as a promising alternative to WiFi and Bluetooth fingerprinting. It is omnipresent, stable, and does not require the deployment of specialized infrastructure to be realized. While several studies have utilized these characteristics in developing indoor positioning systems, the positioning accuracy still can be improved. This paper presents a Convolutional Neural Network (CNN) based method for designing and developing a novel smartwatch-based indoor geomagnetic field positioning system. We tested the proposed system on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.136 meters. We also demonstrate how the Softmax (SM) layer of the network can be exploited to further improve the localization accuracy in user tracking scenarios.