Fingerprint indoor localization technology using geomagnetic sensor array based on crowd-sourcing

ZHANG Yurun; WU Fei; MAO Wankui

doi:10.13873/j.1000-9787(2019)12-0036-04

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Fingerprint indoor localization technology using geomagnetic sensor array based on crowd-sourcing

更新时间：2025-09-11

- Fingerprint indoor localization technology using geomagnetic sensor array based on crowd-sourcing
- Issue 12, Pages: 36-39(2019)
- 作者机构：
  
  上海工程技术大学电子电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2019)12-0036-04
  CLC： TP212;TP391.41
- Published：2019
- 稿件说明：
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ZHANG Yurun, WU Fei, MAO Wankui. Fingerprint indoor localization technology using geomagnetic sensor array based on crowd-sourcing[J]. 2019, (12): 36-39.
DOI：

ZHANG Yurun, WU Fei, MAO Wankui. Fingerprint indoor localization technology using geomagnetic sensor array based on crowd-sourcing[J]. 2019, (12): 36-39. DOI： 10.13873/j.1000-9787(2019)12-0036-04.

摘要

针对有源的室内定位技术应用场景局限

布置设备成本高昂的现状

提出了一种无源高效的基于移动众包方式的地磁指纹定位技术。创新地提出了基于传感器阵列的地磁指纹采集与匹配方法

通过移动众包的方式来解决指纹采集更新工作量大、专业性强的技术适用性难题。通过基于核模糊C均值地磁指纹聚类算法训练离线指纹库

在20次重复实验的情况下

地磁指纹定位平均定位误差为1. 87 m

90%的定位误差小于3 m

较地磁线图匹配算法定位精度及性能均有较大的提升。

Abstract

Aiming at the limitation of application scenarios of active indoor positioning technology and the high cost of layout equipment

a passive and efficient method based on mobile crowd-sourcing is proposed. Firstly

an innovative geomagnetic fingerprint acquisition and matching method based on sensor array is proposed. Solving the problem of technical applicability of large and professional fingerprint collection and update by moving crowdsourcing. Training offline fingerprint database through kernel fuzzy C-means（KFCM） geomagnetic fingerprint clustering algorithm

in the case of 20 replicated experiments

average positioning error of geomagnetic fingerprint positioning is 1. 87 m

90 % positioning error is less than 3 m. Compared with geomagnetic map matching algorithm

positioning precision and performance are greatly improved.

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