Abstract
The accuracy of fingerprint based Bluetooth positioning technology depends on the fingerprint database established in offline phase. However, the change of environment and Access Point (AP) locations has significant impact on wireless signal distribution, resulting a decline in indoor Bluetooth positioning accuracy. In order to solve this problem, this paper presents a fingerprint database updating algorithm. Firstly, RSSI sequence, head, and speed information are extracted from crowdsourcing date. Secondly, the extracted information is used in Pedestrian Dead Reckoning Modification (PDRM) algorithm to get candidate fingerprint. Finally, we propose concepts of standard fingerprint, negative exponential time model, and similarity filtering to update original fingerprint database. The experimental results show that after the proposed fingerprint database updating, fingerprint database positioning accuracy is improved by 0.5 m.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)
Zhuang, Y., Syed, Z., Li, Y., et al.: Evaluation of two WiFi positioning systems based on autonomous crowd sourcing on handheld devices for indoor navigation. IEEE Trans. Mob. Comput. 15(8), 1982–1995 (2016)
Lu, S., Xu, C., Zhong, R.Y., et al.: A RFID-enabled positioning system in automated guided vehicle for smart factories. J. Manuf. Syst. 44(1), 179–190 (2017)
Abd, R.M., El-Rabbany, A.: Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter. Sensors 15(4), 7228–7245 (2015)
Song, L., Zhang, T., Yu, X., et al.: Scheduling in cooperative UWB localization networks using round trip measurements. IEEE Commun. Lett. 20(7), 1409–1412 (2016)
He, S., Lin, W., Chan, S.H.G.: Indoor localization and automatic fingerprint update with altered AP signals. IEEE Trans. Mob. Comput. 16(7), 1897–1910 (2017)
Park, J.G., Charrow, B., Curtis, D., et al.: Growing an organic indoor location system. In: International Conference on Mobile Systems, Applications, and Services, pp. 271–284. ACM, San Francisco (2010)
Huang, J., Millman, D., Quigley, M., et al.: Efficient, generalized indoor WiFi GraphSLAM. In: IEEE International Conference on Robotics and Automation, pp. 1038–1043. IEEE, Shanghai (2011)
Kim, D.H., Hightower, J., Govindan, R., et al.: Discovering semantically meaningful places from pervasive RF-beacons. In: UbiComp, pp. 21–30. ACM, Orlando (2009)
Luo, H., Zhao, F., Jiang, M., et al.: Constructing an indoor floor plan using crowdsourcing based on magnetic fingerprinting. Sensors 17(11), 2678–2692 (2017)
Acknowledgment
This work was supported partly by the Scientific and Technological Research Foundation of Chongqing Municipal Education Commission under grant KJ1704083, the National Natural Science Foundation of China under 61704015, the Fundamental and Frontier Research Project of Chongqing under grant cstc2017jcyjAX0380.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Cong, H., Xie, L., Zhou, M. (2019). An Adaptive Fingerprint Database Updating Scheme for Indoor Bluetooth Positioning. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-19153-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-19153-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19152-8
Online ISBN: 978-3-030-19153-5
eBook Packages: Computer ScienceComputer Science (R0)