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A Fast Offline Database Construction Mechanism for Wi-Fi Fingerprint Based Localization Using Ultra-Wideband Technology

  • Huilin Jie
  • Hao Zhang
  • Kai LiuEmail author
  • Feiyu Jin
  • Chao Chen
  • Chaocan Xiang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1101)

Abstract

With the ever-increasing demand on location-based services (LBS), fingerprint-based methods have attracted more and more attention in indoor localization. However, the considerable overhead of fingerprint is still a problem which hinders the practicability of such technology. Due to the prevalent of Wi-Fi access points (APs) and the high location accuracy of Ultra-Wideband (UWB), in this paper, we propose a hybrid system which utilizes UWB and Wi-Fi technologies to alleviate the offline overhead and improve the localization accuracy. Specifically, we employ UWB to determine the coordinate of each reference point (RP) instead of traditional manual measurement. Meanwhile, the Received Signal Strength Indicator (RSSI) of Wi-Fi is collected by a customized software installed in the mobile device. Then, a timestamp matching scheme is proposed to fuse these data coming from different devices and construct the offline fingerprint database. Besides, in order to better map the online data with offline database, an AP weight assignment scheme is proposed, which allocates APs with different weights based on the RSSI characteristic in each RP. We implement the system in real-world environment and the experimental results demonstrate the effectiveness of the proposed method.

Keywords

Indoor localization Wi-Fi fingerprint UWB technology 

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61872049; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and the Venture & Innovation Support Program for Chongqing Overseas Returnees (Project No. cx2018016).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Huilin Jie
    • 1
  • Hao Zhang
    • 1
  • Kai Liu
    • 1
    Email author
  • Feiyu Jin
    • 1
  • Chao Chen
    • 1
  • Chaocan Xiang
    • 1
  1. 1.Department of Computer ScienceChongqing UniversityChongqingChina

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