Journal of Positioning, Navigation, and Timing (J Position Navig Timing; JPNT)
Citation: Lee, D., Yoo, J., 2023, Indoor Path Recognition Based on Wi-Fi Fingerprints, Journal of Positioning, Navigation, and Timing, 12, 91-100.
Journal of Positioning, Navigation, and Timing (J Position Navig Timing) 2023 June, Volume 12, Issue 2, pages 91-100. https://doi.org/10.11003/JPNT.2023.12.2.91
Received on 24 February 2023, Revised on 21 Mar 2023, Accepted on 27 April 2023, Published on 30 June 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.
Donggyu Lee1, Jaehyun Yoo2†
1Department of Future Convergence Technology Engineering, Sungshin Women’s University, Seoul 02844, Korea
2School of AI Convergence, Sungshin Women’s University, Seoul 02844, Korea
†Corresponding Author: E-mail, jhyoo@sungshin.ac.kr; Tel: +82-920-7695
The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.
indoor path estimation, Wi-Fi fingerprinting, KLDA
Aarons, L. 1993, Sparse data analysis. European journal of drug metabolism and pharmacokinetics, 18, 97-100. https://doi.org/10.1007/BF03220012
Chriki, A., Touati, H., & Snoussi, H. 2017, SVM-based indoor localization in wireless sensor networks, 13th international wireless communications and mobile computing conference (IWCMC), IEEE, 26-30 June 2017, Valencia, Spain. https://doi.org/10.1109/IWCMC.2017.7986446
Dabove, P., Di Pietra, V., Piras, M., Jabbar A. A., & Kazim, S. A. 2018, Indoor positioning using Ultra-wide band (UWB) technologies: Positioning accuracies and sensors’ performances, IEEE/ION Position, Location and Navigation Symposium (PLANS), 23-26 April 2018, Monterey, CA, USA, pp.175-184. https://doi.org/10.1109/PLANS.2018.8373379
Daffertshofer, A., Lamoth, C. J. C., Meijer, O. G., & Beek, P. J. 2004, PCA in studying coordination and variability: a tutorial, Clinical biomechanics, 19, 415-428. https:// doi.org/10.1016/j.clinbiomech.2004.01.005
Hansen, R., Wind, R., Jensen, C. S., & Thomsen, B. 2010, Algorithmic strategies for adapting to environmental changes in 802.11 location fingerprinting, in Proc. IEEE Indoor Positioning and Indoor Navigation (IPIN), 15-17 September 2010, Zurich, Switzerland, pp.1-10. https:// doi.org/10.1109/IPIN.2010.5648270
Jin, G. Y., Lu, X. Y., & Park, M. S. 2006, An indoor localization mechanism using active RFID tag. International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC’06), IEEE, 05-07 June 2006, Taichung, Taiwan. https://doi.org/10.1109/ SUTC.2006.1636157
Kaemarungsi, K. 2005, Efficient design of indoor positioning systems based on location fingerprinting, IEEE, In 2005 International Conference on Wireless Networks, Communications and Mobile Computing, 13-16 June 2005, Maui, HI, USA, pp.181-186. https://doi.org/10.1109/ WIRLES.2005.1549406
Kaune, R. 2012, Accuracy studies for TDOA and TOA localization, 2012 15th International Conference on Information Fusion, 09-12 July 2012, Singapore, pp.408-415.
Keller, J. M., Gray, M. R., & Givens, J. A. 1985, A fuzzy k-nearest neighbor algorithm, IEEE transactions on systems, man, and cybernetics, SMC-15, 580-585. https://doi.org/10.1109/TSMC.1985.6313426
Leu, J.-S. & Tzeng, H.-J. 2012, Received signal strength fingerprint and footprint assisted indoor positioning based on ambient Wi-Fi signals, in Proc. IEEE Veh. Technol. Conf. (VTC Spring), 06-09 May 2012, Yokohama, Japan, pp.1-5. https://doi.org/10.1109/ VETECS.2012.6239883
Lu, C., Uchiyama, H., Thomas, D., Shimada, A., & Taniguchi, R. 2019, Indoor Positioning System Based on Chest-Mounted IMU, Sensors, 19, 420. https://doi. org/10.3390/s19020420
Mainetti, L., Patrono, L., & Sergi, I. 2014, A survey on indoor positioning systems, IEEE, 22nd international conference on software, telecommunications and computer networks (SoftCOM), 17-19 September 2014, Split, Croatia, pp.111-120. https://doi.org/10.1109/ SOFTCOM.2014.7039067
Mendoza-Silva, G. M., Costa, A. C., Torres-Sospedra, J., Painho, M., & Huerta, J. 2022, Environment-aware regression for indoor localization based on WiFi fingerprinting, IEEE Sensors Journal, 22, 4978-4988. https://doi.org/10.1109/JSEN.2021.3073878
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. 1999, Fisher discriminant analysis with kernels, Neural networks for signal processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), 25 August 1999, Madison, WI, USA. https://doi.org/10.1109/NNSP.1999.788121
Parlett, B. N. 1998, The symmetric eigenvalue problem (Philadelphia, PA: Society for industrial and Applied Mathematics). https://doi.org/10.1137/1.9781611971163
So, J., Lee, J. Y., Yoon, C. H., & Park, H. 2013, An improved location estimation method for WiFi fingerprint-based indoor localization, International Journal of Software Engineering, and Its Applications, 7, 77-86. https:// www.earticle.net/Article/A208533
Xia, S., Liu, Y., Yuan, G., Zhu, M., & Wang, Z. 2017, Indoor fingerprint positioning based on Wi-Fi: An overview, ISPRS International Journal of Geo-Information, 6, 135. https://doi.org/10.3390/ijgi6050135
Xu, J., Ma, M., & Law, C. L. 2008, AOA Cooperative Position Localization, IEEE GLOBECOM 2008 – 2008 IEEE Global Telecommunications Conference, 30 November – 04 December 2008, New Orleans, LA, USA, pp.1-5. https:// doi.org/10.1109/GLOCOM.2008.ECP.720
Yamasaki, R., Ogino, A., Tamaki, T., Uta, T., Matsuzawa, N., et al. 2005, TDOA location system for IEEE 802.11b WLAN, IEEE Wireless Communications and Networking Conference, 13-17 March 2005, New Orleans, LA, pp.2338-2343. https://doi.org/10.1109/WCNC.2005.1424880
Yoo, J.-H. 2020, Semi-supervised Generative Adversarial Network for Wi-Fi Fingerprint Based Indoor Location Awareness, Journal of Institute of Control, Robotics and Systems, 26, 1116-1121. https://doi.org/10.5302/ J.ICROS.2020.20.0151
Zhuang, Y., Yang, J., Li, Y., Qi, L., & El-Sheimy, N. 2016, Smartphone-based indoor localization with Bluetooth low energy beacons, Sensors, 16, 596. https://doi.org/10.3390/ s16050596
Conceptualization, J.Y.; methodology, D.L.; software, D.L.; validation, D.L.; formal analysis, J.Y.; investigation, J.Y.; experiment, D.L.; writing—original draft preparation, D.L.; writing—review and editing, J.Y.; funding acquisition, J.Y.
The authors declare no conflict of interest.