Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform - Journal of Computer and Communications

JCC > Vol.5 No.3, March 2017

Journal of Computer and Communications

Volume 5, Issue 3 (March 2017)

ISSN Print: 2327-5219 ISSN Online: 2327-5227

Google-based Impact Factor: 1.12 Citations

Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform ()

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DOI: 10.4236/jcc.2017.53010 1,144 Downloads 1,877 Views Citations

Author(s)

Zhibin Meng^1,2, Mei Wang^1,2, Enliang Wang^1,2, Xiangyu Xu^1,2

Affiliation(s)

¹Key Laboratory of Cognitive Radio &Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin, China.
²Guangxi Experiment Center of Information Science, Guilin, China.

ABSTRACT

The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.

KEYWORDS

Indoor Localization, Magnetic, Particle Filter, Robust Local Weighted Regression Algorithm

Share and Cite:

Meng, Z. , Wang, M. , Wang, E. and Xu, X. (2017) Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform. Journal of Computer and Communications, 5, 80-90. doi: 10.4236/jcc.2017.53010.

Cited by

[1]	Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones
	2020

[2]	MeshMap: A Magnetic Field-Based Indoor Navigation System With Crowdsourcing Support
	2020

[3]	A Comprehensive Analysis of Magnetic Field Based Indoor Positioning With Smartphones: Opportunities, Challenges and Practical Limitations
	2020

[4]	Learning Sequence-Based Fingerprint for Magnetic Indoor Positioning System
	2019

[5]	mPILOT-magnetic field strength based pedestrian indoor localization
	Sensors, 2018

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