Journal of Positioning, Navigation, and Timing (J Position Navig Timing; JPNT)
Indexed in KCI (Korea Citation Index)
OPEN ACCESS, PEER REVIEWED
pISSN 2288-8187
eISSN 2289-0866

Indoor Positioning Technology Integrating Pedestrian Dead Reckoning and WiFi Fingerprinting Based on EKF with Adaptive Error Covariance

PDF

CONTENTS

Research article

Citation: Cho, E. Y., Kwon, J. U., Chae, M. S., Cho, S. Y., Yoo, J., & Seo, S., 2023, Indoor Positioning Technology Integrating Pedestrian Dead Reckoning and WiFi Fingerprinting Based on EKF with Adaptive Error Covariance, Journal of Positioning, Navigation, and Timing, 12, 271-280.

Journal of Positioning, Navigation, and Timing (J Position Navig Timing) 2023 September, Volume 12, Issue 3, pages 271-280. https://doi.org/10.11003/JPNT.2023.12.3.271

Received on 15 August 2023, Revised on 02 September 2023, Accepted on 05 September 2023, Published on 30 September 2023.

License: Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Indoor Positioning Technology Integrating Pedestrian Dead Reckoning and WiFi Fingerprinting Based on EKF with Adaptive Error Covariance

Eui Yeon Cho1, Jae Uk Kwon1, Myeong Seok Chae1, Seong Yun Cho2,3†, JaeJun Yoo4†, SeongHun Seo4

1Department of IT Engineering, Kyungil University, Gyeongsan 38428, Korea

2School of Smart Design Engineering, Kyungil University, Gyeongsan 38428, Korea

3NavIn Labs Co., Ltd. Daegu 41066, Korea 4Mobility UX Section, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea

†Corresponding Author: E-mail, sycho@kiu.kr, jjryu@etri.re.kr; Tel: +82-53-600-5584, +82-42-860-1011

Abstract

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

Keywords

indoor positioning, PDR, WiFi fingerprinting, integrated positioning, adaptive EKF

References

Cho, S. Y., Lee, J. H., & Park, C. G. 2022, A Zero-Velocity Detection Algorithm Robust to Various Gait Types for Pedestrian Inertial Navigation, IEEE Sensors Journal, 22, 4916-4931. https://doi.org/10.1109/JSEN.2021.3064087

Cho, S. Y. & Park, C. G. 2006, MEMS Based Pedestrian Navigation System, The Journal of Navigation, 59, 135153. https://doi.org/10.1017/S0373463305003486

He, S. & Chan, S. H. G. 2016, Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons, IEEE Communications Surveys & Tutorials, 18, 466-490. https://doi.org/10.1109/COMST.2015.2464084

Kim, B., Bong, W., & Kim, Y. C. 2011, Indoor Localization for Wi-Fi Devices by Cross-Monitoring AP and Weighted Triangulation, IEEE Consumer Communications and Networking Conference (CCNC), 933-936. https://doi. org/10.1109/CCNC.2011.5766644

Kwon, J. U., Chae, M. S., & Cho, S. Y. 2022, Improved LTE Fingerprint Positioning Through Clustering-based Repeater Detection and outlier Removal, JPNT, 11, 369379. https://doi.org/10.11003/JPNT.2022.11.4.369

Li, X., Wei, D., Lai, Q., Xu, Y., & Yuan, H. 2017, SmartphoneBased Integrated PDR/GPS/ Bluetooth Pedestrian Location, Advances in Space Research, 59, 877-889. https://doi.org/10.1016/j.asr.2016.09.010

Yuan, D., Zhang, J., Wang, J., Cui, X., Liu, F., et al., 2021, Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint, Sensors, 21, 4390. https://doi.org/10.3390/s21134390

Author contributIons

Eui Yeon Cho contributed to the design and implementation of the PDR and integration algorithms and to the writing of the manuscript. Jae Uk Kwon contributed to the design and implementation of the WiFi fingerprinting algorithm. Myeong Seok Chae contributed to provide information in writing the manuscript. Seong Yun Cho led the research and reviewed the manuscript as the person in charge of the service project. Jae Jun Yoo and Seong Hun Seo supervised the research as original project managers and provided related information.

Conflicts of interest

The authors declare no conflict of interest.