Abstract
In this study, we propose a novel mobile tracking scheme which utilizes the fuzzy-based decision making with the consideration of the information such as previous location, moving direction and distance to the base station as well as received signal strength, thereby resulting to the estimation performance even much better than the previous schemes. Our scheme divides a cell into many blocks based on the signal strength and then estimate in stepwise the optimal block where a mobile locates using Multi-Criteria Decision Making (MCDM). Through numerical results, we show that our proposed mobile tracking method provides a better performance than the conventional method using the received signal strength.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Djuknic, G.M., Richton, R.E.: Geolocation and Assisted GPS. IEEE Computer 34(2), 123–125 (2001)
Figel, W.G., Shepherd, N.H., Trammell, W.F.: Vehicle location by a signal attenuation method. IEEE Trans. Veh. Technol. VT-18, 104–109 (1969)
Ott, G.D.: Vehicle location in cellular mobile radio systems. IEEE Trans. Veh. Tech. VT-26, 43–46 (1977)
Hatta, M., Nagatsu, T.: Mobile Location Using Signal Strength Measurements in a Cellular System. IEEE Transactions on Vehicular Technology VT29, 245–252 (1980)
Song, H.L.: Automatic Vehicle Location in Cellular Communication Systems. IEEE Transactions on Vehicular Technology 43, 902–908 (1994)
Kennemann, O.: Pattern Recognition by Hidden Markov Models for Supporting Handover Decisions in the GSM system. In: Proc. 6 th Nordic Seminar Dig. Mobile Radio Comm., Stockholm, Sweden, pp. 195–202 (1994)
Nypan, T., Hallingstad, O.: Cellular Positioning by Database Comparison and Hidden Markov Models. In: PWC 2002, October 2002, pp. 277–284 (2002)
Kennemann, O.: Continuous Location of Moving GSM Mobile Stations by Pattern Recognition Techniques. In: Proc. 5th Int. Symp. Personal, Indoor, Mobile, Radio Comm., pp. 630–634. den Haag, Holland (1994)
Staras, H., Honikman, S.N.: The accuracy of vehicle location by trilateration in a dense urban environment. IEEE Trans. Veh. Tech. VT-26, 38–43 (1972)
Rappaport, T.S., Reed, J.H., Woerner, B.D.: Position Location Using Wireless Communications on Highways of the Future. IEEE Communications Magazine, pp. 33–41 (October)
Spirito, Y.A.: On the Accuracy of Cellular Mobile Station Location Estimation. IEEE Trans. Veh. Technol. 50(3), 674–685 (2001)
Lee, J.C., Mun, Y.S.: Mobile Location Estimation Scheme. SK telecommunications Review 9(6), 968–983 (1999)
Naso, C., Turchiano, B.: A Fuzzy Multi-Criteria Algorithm for Dynamic Routing in FMS. In: IEEE ICSMC 1998, vol. 1, pp. 457–462 (October 1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 IFIP International Federation for Information Processing
About this paper
Cite this paper
Lee, J., Yoo, SJ., Lee, D.C. (2004). Fuzzy Logic Adaptive Mobile Location Estimation. In: Jin, H., Gao, G.R., Xu, Z., Chen, H. (eds) Network and Parallel Computing. NPC 2004. Lecture Notes in Computer Science, vol 3222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30141-7_92
Download citation
DOI: https://doi.org/10.1007/978-3-540-30141-7_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23388-6
Online ISBN: 978-3-540-30141-7
eBook Packages: Springer Book Archive