Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-25T19:20:39.539Z Has data issue: false hasContentIssue false
  • English
  • Français

An Improved Fingerprint Algorithm with Access Point Selection and Reference Point Selection Strategies for Indoor Positioning

Published online by Cambridge University Press:  14 July 2020

Changgeng Li ,
Hui Huang  and
Bowen Liao
Changgeng Li*
Affiliation:
(School of Physics and Electronics, Central South University, Changsha, China)
Hui Huang
Affiliation:
(School of Physics and Electronics, Central South University, Changsha, China)
Bowen Liao
Affiliation:
(School of Physics and Electronics, Central South University, Changsha, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The fingerprint positioning (FP) algorithm has been investigated extensively owing to the fact that it can provide a relatively ideal indoor positioning result. However, the effectiveness of the fingerprint algorithm relies on the size of fingerprint database, which prevents the algorithm from being widely applied in practical applications. In this paper, an improved fingerprint algorithm with access point (AP) selection strategy and reference point (RP) selection strategy is proposed to reduce the size of the fingerprint database and improve the positioning accuracy. The experimental results show that the proposed algorithm can reduce the storage size of the fingerprint database by more than 42·64%. Moreover, compared with the FP algorithm, the fingerprint algorithm with segment characteristic distance (FP-SCD) and the fingerprint algorithm with RP selection strategy (FP-RPSS), the average positioning error of the proposed algorithm is reduced by 20·15%, 10·83% and 11·57%, respectively. Therefore, the proposed algorithm has a good application in real positioning scenarios.

Keywords

Indoor positioningFingerprint algorithmAP selection strategyRP selection strategy
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 6 , November 2020 , pp. 1182 - 1201
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bahl, P. and Padmanabhan, V. N. (2000). RADAR: An In-Building RF-based User Location and Tracking System. INFOCOM 2000. Proceedings of the Nineteenth Joint Conference of the IEEE Computer and Communications Societies, Vol. 2, 775–784.CrossRefGoogle Scholar
Cai, X., Li, X., Yuan, R., and Hei, Y. (2015). Identification and mitigation of NLOS based on channel state information for indoor WiFi localization. 2015 International Conference on Wireless Communications & Signal Processing (WCSP), Nanjing, China.CrossRefGoogle Scholar
Chang, Q., Chen, W., Hou, H., and Wang, W. (2015). A novel clustering and KWNN-based strategy for Wi-Fi fingerprint indoor localization. 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), Harbin, China.Google Scholar
Chen, Y., Yang, Q., Yin, J., and Chai, X. (2006). Power-efficient access-point selection for indoor location estimation. IEEE Transactions on Knowledge and Data Engineering, 18(7), 877–888.CrossRefGoogle Scholar
Choi, K. H., Ra, W. S., Park, S. Y., and Jin, B. P. (2013). Robust least squares approach to passive target localization using ultrasonic receiver array. IEEE Transactions on Industrial Electronics, 61(4), 1993–2002.10.1109/TIE.2013.2266076CrossRefGoogle Scholar
DiGiampaolo, E. and Martinelli, F. (2014). Mobile robot localization using the phase of passive UHF RFID signals. IEEE Transactions on Industrial Electronics, 61(1), 365–376.10.1109/TIE.2013.2248333CrossRefGoogle Scholar
Jhuang, F. M., Hung, C. F., Tuan, C. C., Wu, Y. C., and Leu, F. Y. (2015). An AP selection with RSS standard deviation for indoor positioning in Wi-Fi. 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Blumenau, Brazil.CrossRefGoogle Scholar
Jing, H., Pinchin, J., Hill, C., and Moore, T. (2016). An adaptive weighting based on modified DOP for collaborative indoor positioning. Journal of Navigation, 69(2), 225–245.CrossRefGoogle Scholar
Jung, S. Y., Hann, S. and Park, C. S. (2012). TDOA-based optical wireless indoor localization using led ceiling lamps. IEEE Transactions on Consumer Electronics, 57(4), 1592–1597.CrossRefGoogle Scholar
Kaemarungsi, K. and Krishnamurthy, P. (2012). Analysis of WLAN's received signal strength indication for indoor location fingerprinting. Pervasive and Mobile Computing, 8(2), 292–316.CrossRefGoogle Scholar
Li, C., Qiu, Z. and Liu, C. (2017). An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning. Wireless Personal Communications, 96(2), 2239–2251.CrossRefGoogle Scholar
Shin, B., Lee, J. H., Lee, T., and Kim, H. S. (2012). Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems. 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT), Seoul, South Korea.Google Scholar
Wu, F., Liang, Y., Fu, Y., and Geng, C. (2017). A new indoor positioning system using artificial encoded magnetic 506 fields. Journal of Navigation, 71(2), 299–316.CrossRefGoogle Scholar
Xue, W., Hua, X., Li, Q., Yu, K., and Qiu, W. (2018). Improved neighboring reference points selection method for WiFi-based indoor localization. IEEE Sensors Letters, 2(2), 2475–1472.CrossRefGoogle Scholar
Yang, C. and Shao, H. (2015). WiFi-based indoor positioning. IEEE Communications Magazine, 53(3), 150–157.CrossRefGoogle Scholar
Zhang, W., Hua, X., Yu, K., Qiu, W., and Zhang, S. (2016). Domain clustering based WiFi indoor positioning algorithm. 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain.CrossRefGoogle Scholar
Zou, H., Luo, Y., Lu, X., Jiang, H., and Xie, L. (2015). A mutual information based online access point selection strategy for WiFi indoor localization. 2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden.CrossRefGoogle Scholar