Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語

Template
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 31, Number 10(3) (2019)
Copyright(C) MYU K.K.
pp. 3303-3318
S&M2012 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2460
Published in advance: September 2, 2019
Published: October 31, 2019

MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone [PDF]

Kai-Yue Qiu, He Huang, Wei Li, and De-An Luo

(Received May 27, 2019; Accepted August 9, 2019)

Keywords: pedestrian dead reckoning, particle filters, indoor positioning, geomagnetic fingerprint matching, smartphone inertial sensor

Geomagnetic positioning technology has proven to be worth investigating in the field of location-based services (LBSs), but the positioning of geomagnetic technology alone will generate a certain amount of error. To overcome the ambiguity of single-point geomagnetic data, we developed a geomagnetic indoor navigation system, magnetic + particle filters + pedestrian dead reckoning (MagPP) based on the pedestrian dead reckoning (PDR) algorithm using a smartphone as a hardware platform. We calculate the measurement trajectory contour of the PDR to solve the gross error of the magnetic field sequence (MFS). The mean square error criterion of the matching trajectory is established, and the particle filter (PF) algorithm is used to realize the iterative calculation of the real-time correction of the PDR cumulative error. In the test, with an area of 68 × 1.8 m2, the experimental results produced an average positioning error of 1.13 m and a maximum positioning error of 2.17 m. The positioning of the fusion algorithm proposed in this paper is 42% higher than that of the PDR algorithm alone. Compared with the single geomagnetic fingerprint-matching algorithm for indoor positioning, the positioning accuracy is improved by 57%. Therefore, the MagPP algorithm significantly improved indoor positioning.

Corresponding author: He Huang


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Kai-Yue Qiu, He Huang, Wei Li, and De-An Luo, MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone, Sens. Mater., Vol. 31, No. 10, 2019, p. 3303-3318.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Sensors and Materials: Emerging Investigators in Japan
Guest editor, Tsuyoshi Minami (The University of Tokyo)


Special issue on Novel Materials and Sensing Technologies on Electronic and Mechanical Devices (2)-1
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Hsien-Wei Tseng (Longyan University)


Special Issue on Materials, Devices, Circuits, and Analytical Methods for Various Sensors (4)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Cheng-Hsing Hsu (National United University), Ja-Hao Chen (Feng Chia University), and Wei-Ling Hsu (Huaiyin Normal University)
Conference website


Special issue on Novel Materials and Sensing Technologies on Electronic and Mechanical Devices (2)-2
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Hsien-Wei Tseng (Longyan University)


Special Issue on New Trends in Robots and Their Applications
Guest editor, Ikuo Yamamoto (Nagasaki University)


Special Issue on Artificial Intelligence in Sensing Technologies and Systems
Guest editor, Prof. Lin Lin (Dalian University of Technology)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.