Abstract
The accurate geo-localization of mobile devices based upon received signal strength (RSS) in an urban area is hindered by obstacles in the signal propagation path. Current localization methods have their own advantages and drawbacks. Triangular lateration (TL) is fast and scalable but employs a monotone RSS-to-distance transformation that unfortunately assumes mobile devices are on the line of sight. Radio frequency fingerprinting (RFP) methods employ a reference database, which ensures accurate localization but unfortunately hinders scalability.
Here, we propose a new, simple, and robust method called lookup lateration (LL), which incorporates the advantages of TL and RFP without their drawbacks. Like RFP, LL employs a dataset of reference locations but stores them in separate lookup tables with respect to RSS and antenna towers. A query observation is localized by identifying common locations in only associating lookup tables. Due to this decentralization, LL is two orders of magnitude faster than RFP, making it particularly scalable for large cities. Moreover, we show that analytically and experimentally, LL achieves higher localization accuracy than RFP as well. For instance, using grid size 20 m, LL achieves 9.11 m and 55.66 m, while RFP achieves 72.50 m and 242.19 m localization errors at 67% and 95%, respectively, on the Urban Hannover Scenario dataset.
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
Notes
- 1.
Defined by Kronecker delta (\(\delta _{a,b} = 1 \iff a=b\), otherwise 0).
References
Directive 2002/58/EC on privacy and electronic communications
Bahl, P., Padmanabhan, V.N.: Radar: an in-building RF-based user location and tracking system. In: Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)
Campos, R.S., Lovisolo, L.: A fast database correlation algorithm for localization of wireless network mobile nodes using coverage prediction and round trip delay. In: VTC Spring. IEEE (2009)
Campos, R.S., Lovisolo, L.: Mobile station location using genetic algorithm optimized radio frequency fingerprinting. In: Proceedings of ITS 2010 International Telecommunications Symposium (2010)
Cong, L., Zhuang, W.: Nonline-of-sight error mitigation in mobile location. IEEE Trans. Wirel. Commun. 4(2), 560–573 (2005)
FCC: Revision of the commission rule to ensure compatibility with enhanced 911 emergency calling system. Technical report RM-8143, Federal Communications Commission (FCC), Washington, DC (2015)
Gentile, C., Alsindi, N., Raulefs, R., Teolis, C.: Geolocation Techniques: Principles and Applications. Springer, Heidelberg (2012)
Gezici, S.: A survey on wireless position estimation. Wirel. Pers. Commun. 44(3), 263–282 (2008)
He, S., Chan, S.H.G.: Tilejunction: mitigating signal noise for fingerprint-based indoor localization. IEEE Trans. Mobile Comput. 15(6), 1554–1568 (2016)
Ma, C., Klukas, R., Lachapelle, G.: A nonline-of-sight error-mitigation method for TOA measurements. IEEE Trans. Veh. Technol. 56(2), 641–651 (2007)
Magro, M.J., Debono, C.J.: A genetic algorithm approach to user location estimation in UMTS networks. In: The International Conference on “Computer as a Tool”, EUROCON 2007, pp. 1136–1139. IEEE (2007)
Marco, A., Casas, R., Asensio, A., Coarasa, V., Blasco, R., Ibarz, A.: Least median of squares for non-line-of-sight error mitigation in GSM localization. In: 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5, September 2008
Neskovic, A., Neskovic, N., Paunovic, G.: Modern approaches in modeling of mobile radio systems propagation environment. IEEE Commun. Surv. Tutorials 3(3), 2–12 (2000)
Nuño-Barrau, G., Páz-Borrallo, J.M.: A new location estimation system for wireless networks based on linear discriminant functions and hidden Markov models. EURASIP J. Appl. Signal Process. 2006, 159–159 (2006)
Rose, D.M., Jansen, T., Werthmann, T., Türke, U., Kürner, T.: The IC 1004 urban hannover scenario - 3D pathloss predictions and realistic traffic and mobility patterns (2013)
Takenga, C., Xi, C., Kyamakya, K.: A hybrid neural network-data base correlation positioning in GSM network. In: 2006 10th IEEE Singapore International Conference on Communication Systems, pp. 1–5, October 2006
Takenga, C.M., Kyamakya, K.: Location fingerprinting in GSM network and impact of data pre-processing (2006)
Wu, C.L., Fu, L.C., Lian, F.L.: WLAN location determination in e-home via support vector classification. In: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 1026–1031. IEEE (2004)
Yu, L., Laaraiedh, M., Avrillon, S., Uguen, B.: Fingerprinting localization based on neural networks and ultra-wideband signals. In: 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 184–189, December 2011
Zekavat, R., Buehrer, R.M.: Handbook of Position Location: Theory, Practice and Advances, 1st edn. Wiley/IEEE Press, Hoboken/Piscataway (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shestakov, A., Doroshin, D., Shmelkin, D., Kertész-Farkas, A. (2018). Lookup Lateration: Mapping of Received Signal Strength to Position for Geo-Localization in Outdoor Urban Areas. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2018. Lecture Notes in Computer Science(), vol 11179. Springer, Cham. https://doi.org/10.1007/978-3-030-11027-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-11027-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11026-0
Online ISBN: 978-3-030-11027-7
eBook Packages: Computer ScienceComputer Science (R0)