Fingerprint-based Wi-Fi indoor localization using map and inertial sensors

Xingwang Wang,Xuan Du,Xiaohui Wei,Yuanyuan Liu,Kun Yang

doi:10.1177/1550147717749817

Abstract

It is a common understanding that the localization accuracy can be improved by indoor maps and inertial sensors. However, there is a lack of concrete and generic solutions that combine these two fe...

Highlights

In recent years, with the development of Internet of Things (IoT) applications and mobile computing, there is a growing concern about indoor location information
Given that many buildings are equipped with Wi-Fi access points (APs), Wi-Fi localization becomes much acceptable by users
We propose that the reference points placement theory in section ‘‘Reference points placement theory’’ to prove localization accuracy will not continue improved by adding new reference points when distances among reference points are less than a lower bound

Summary

Introduction

With the development of Internet of Things (IoT) applications and mobile computing, there is a growing concern about indoor location information. Researchers assume that the positioning accuracy of fingerprint-based indoor localization method depends on the number of reference points in the database.[16] A larger database consumes more training time and computational resources. This is a pseudo-proposition and we give a theoretically proof in this work. We propose that the reference points placement theory in section ‘‘Reference points placement theory’’ to prove localization accuracy will not continue improved by adding new reference points when distances among reference points are less than a lower bound Leveraging this theory, indoor localization systems can achieve a high accuracy with less training time by setting a minimum distance (noise distance) between reference points. The localization accuracy is evaluated and compared with the previous Wi-Fi indoor localization systems in section ‘‘Experiment.’’ we give a brief conclusion in section ‘‘Conclusion.’’

Related work

Conclusion