The Improvement of Traditional Indoor Localization Model Using Magnetic Field Based on Smartphone

Abstract

The stability of geomagnetism can be used as an indoor positioning fingerprint mark, which has good precision and applicability, therefore, the study of geomagnetism has become an emerging direction of indoor positioning in recent years. In the existing research, the use of geomagnetism mostly follows the idea of building a geomagnetic fingerprint map and then real-time similarity matching online. However, there are still many new ideas for the application of geomagnetism. In this paper, based on the research of the existing geomagnetic framework, two optimizations are made. One is a geomagnetic data migration(GDM) model based on data similarity. The model is mainly for the difference of the built-in geomagnetic sensor of different mobile phone models. When the indoor environment does not change greatly, the standard geomagnetic acquisition sensor is used for one acquisition, other types of mobile phones use the similarity matching model to calculate the geomagnetic fingerprint map without first acquiring geomagnetism in advance, thereby performing indoor positioning. The other is a step counting optimization model based on geomagnetic assisted accelerometer(GASC), a geomagnetic dynamic threshold method is proposed by data mining of shaking mobile phone and geomagnetic variation trend while walking. By combining with the traditional accelerometer threshold method model, the pseudo step counting recognition ability is improved. The experimental results show that the optimized model performs better anti-interference in the case of shaking the mobile phone.