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Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals

Authors:

H. Leppäkoski,

J. TakalaAuthors Info & Claims

Journal of Signal Processing Systems, Volume 71, Issue 3

Pages 287 - 296

https://doi.org/10.1007/s11265-012-0711-5

Published: 01 June 2013 Publication History

Abstract

As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors. The results with different combinations of the available sensor information are compared.

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Cited By

Abadleh AAl-Mahadeen BAlNaimat RLasassmeh O(2021)Noise segmentation for step detection and distance estimation using smartphone sensor dataWireless Networks10.1007/s11276-021-02588-027:4(2337-2346)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s11276-021-02588-0
Zhou ZMo SWu JFourati H(2019)Behaviors classification based distance measuring system for pedestrians via a foot‐mounted inertial sensorAsian Journal of Control10.1002/asjc.200521:4(1483-1495)Online publication date: 8-Oct-2019
https://dl.acm.org/doi/10.1002/asjc.2005
Huang BQi GYang XZhao LZou HLukowicz PKrüger ABulling ALim YPatel S(2016)Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphonesProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2971648.2971742(374-385)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2971648.2971742
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Information & Contributors

Information

Published In

cover image Journal of Signal Processing Systems

Journal of Signal Processing Systems Volume 71, Issue 3

June 2013

139 pages

ISSN:1939-8018

EISSN:1939-8115

Issue’s Table of Contents

Copyright © Copyright © 2013 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2013

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Cited By

Abadleh AAl-Mahadeen BAlNaimat RLasassmeh O(2021)Noise segmentation for step detection and distance estimation using smartphone sensor dataWireless Networks10.1007/s11276-021-02588-027:4(2337-2346)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s11276-021-02588-0
Zhou ZMo SWu JFourati H(2019)Behaviors classification based distance measuring system for pedestrians via a foot‐mounted inertial sensorAsian Journal of Control10.1002/asjc.200521:4(1483-1495)Online publication date: 8-Oct-2019
https://dl.acm.org/doi/10.1002/asjc.2005
Huang BQi GYang XZhao LZou HLukowicz PKrüger ABulling ALim YPatel S(2016)Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphonesProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2971648.2971742(374-385)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2971648.2971742
Wu ZJedari EMuscedere RRashidzadeh R(2016)Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localizationComputer Communications10.1016/j.comcom.2016.03.00183:C(64-71)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.comcom.2016.03.001
Stahl CKrieg-Brückner BZagler WGöttfried BJonas SSirazitdinova ELensen JKochanov DMayzek Hde Heus THouben RSlijp HDeserno T(2015)IMAGOJournal of Ambient Intelligence and Smart Environments10.3233/AIS-1503347:5(679-692)Online publication date: 4-Sep-2015
https://dl.acm.org/doi/10.3233/AIS-150334

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