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Journal of Sensors Volume 2016 ,2016-12-13
Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning
Research Article
Wenhua Shao 1 Fang Zhao 1 Cong Wang 1 Haiyong Luo 2 Tunio Muhammad Zahid 1 Qu Wang 2 Dongmeng Li 2
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DOI:10.1155/2016/1945695
Received 2016-05-28, accepted for publication 2016-10-30, Published 2016-10-30
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摘要

Smartphone based indoor positioning has greatly helped people in finding their positions in complex and unfamiliar buildings. One popular positioning method is by utilizing indoor magnetic field, because this feature is stable and infrastructure-free. In this method, the magnetometer embedded on the smartphone measures indoor magnetic field and queries its position. However, the environments of the magnetometer are rather harsh. This harshness mainly consists of coarse-grained hard/soft-iron calibrations and sensor electronic noise. The two kinds of interferences decrease the position distinguishability of the magnetic field. Therefore, it is important to extract location features from magnetic fields to reduce these interferences. This paper analyzes the main interference sources of the magnetometer embedded on the smartphone. In addition, we present a feature distinguishability measurement technique to evaluate the performance of different feature extraction methods. Experiments revealed that selected fingerprints will improve position distinguishability.

授权许可

Copyright © 2016 Wenhua Shao et al. 2016
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

图表

Stable and local disturbance of indoor magnetic field. This is the MFM of a 55-meter-long corridor collected in constant speed. The offset between the two signals will be explained in Section 5.2.

Soft- and hard-iron effect illustration.

Parallel versus rotation data collection illustration.

Magnetic magnitude in different attitudes.

Static magnetic magnitude collection statistics. The phone was put up on a tree in a park, sampling magnetic data for 55 seconds.

Fingerprints similarity with compare length 2 m (a) and compare length 6 m (b). The dash curve of f1 is real-time fingerprint, and that of f2 is magnetic localization model. The bold blue curves are fingerprint segments to be compared. The bold red curves are relative similar segments in the localization model.

Fingerprint distinguishability with compare length 2 m (a) and compare length 6 m (b).

Fingerprint distinguishability with compare length 2 m (a) and compare length 6 m (b).

Fingerprint distinguishability and error distance under different compare lengths.

MFM wavelet transform.

Haar wavelet transform fingerprint. The vertical heads and tails are caused by Haar wavelet.

Savitzky–Golay filter fingerprint.

Moving average filter fingerprint.

Wavelet denoising fingerprint.

Magnitude response.

Butterworth filter fingerprint.

Mean fingerprint distinguishability under different compare lengths. Two thousand random segments calculate the result for each kind of fingerprint.

MFM statistics of different devices in the same place. Mean statistics (a), standard deviation statistics (b).

MFM statistics of different devices in the same place. Mean statistics (a), standard deviation statistics (b).

MFM statistics of different places by the same smartphone. Mean statistics (a), standard deviation statistics (b).

MFM statistics of different places by the same smartphone. Mean statistics (a), standard deviation statistics (b).

Static magnetometer measurement statistics of different smartphones in the same place.

Static magnetometer measurement distribution of different places by the same smartphone.

Wavelet fingerprint extraction by different analyzing wavelets.

Savitzky–Golay fingerprint extraction by different polynomial orders.

Average moving fingerprint extraction by different average lengths.

Wavelet denoising fingerprint extraction by different analyzing wavelets.

Butterworth LPF fingerprint extraction by different low pass bands.

Diversity fingerprints comparison.

Confusion matrix of original MFM signal.

Confusion matrix of Butterworth fingerprint.

Confusion matrix of subtracted signal.

通讯作者

Wenhua Shao.School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China, bupt.edu.cn.shaowenhua@ict.ac.cn

推荐引用方式

Wenhua Shao,Fang Zhao,Cong Wang,Haiyong Luo,Tunio Muhammad Zahid,Qu Wang,Dongmeng Li. Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning. Journal of Sensors ,Vol.2016(2016)

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