TDOA Wireless Localization Comparison Influence of Network Topology
DOI:
https://doi.org/10.14738/tnc.25.515Keywords:
Wireless NetworkAbstract
The interest to wireless positioning techniques has been increasing in recent decades due to wide spread of location-based services as well as constraints imposed by regulator on cellular operator to achieve an accepted level of cellular accuracy regardless of availability of GPS signals. Nevertheless, failure of some base stations cannot be fully avoided, yielding various cellular topologies, which, in turn would likely influence the accuracy of the positioning. This paper explores four types of cellular topologies: balanced, circular, U-shape and linear, which can be inferred from balanced topology structure. Assuming time difference of arrival technology and, up to some extent, time of arrival technology were employed, least square like methods are contrasted with maximum likelihood, Taylor, Chan and hybrid approaches in a simulation platformReferences
C.C. Docket, Revision of the Commission rules to ensure compatibility with enhanced 911 emergency calling systems, RM-8143, Report No. 94-102, FCC, 1994
B. Brumitt, B. Meyers, J. Krumm, A. Kern, and S. Shafer. EasyLiving: Technologies for intelligent environments, Proceeding of the second international symposium on Handheld and Ubiquitous Computing, Bristol, UK
G. Abowd, K. Lyons, and K. Scott. The Rhino project, Aug. 1998. http://www.cc.gatech.edu/fce/uvid/rhino.html
G. D. Abowd, C. G. Atkeson, J. Hong, S. Long, R. Kooper, and M. Pinkerton. Cyberguide: a mobile context-aware tour guide. Wireless Networks, 3(5):421–433, Oct. 1997.
P. Bahl and V. N. Padmanabhan. Enhancements to the RADAR user location and tracking system. Technical Report MSR-TR-2000-12, Microsoft Research, Feb. 2000
J. Small, A. Smailagic, and D. P. Siewiorek. Determining user location for context aware computing through the use of a wireless LAN infrastructure, Dec. 2000. http://www-2.cs.cmu.edu/˜aura/docdir/small00.pdf
A. Smailagic, D. Siewiorek, J. Anhalt, D. Kogan, and Y. Wang. Location sensing and privacy in a context aware computing environment. Pervasive Computing, 2001
I. Guvenc and CC Chong, A survey on TOA Based Wireless Localization and NLOS Mitigation Techniques, IEEE Communication Surveys and Tutorials, 11 (3), 2009, 107-124.
J. P. McGeehan, and H.R. Anderson. Optimizing Microcell Base Station Locations Using Simulated Annealing Techniques. In: Proc. of the IEEE Vehicular Technology Conference,1994, pp. 858–862
P. Reininger, and A. Caminada, .Model for GSM Radio Network Optimisation. In: 2nd Intl. ACM/IEEE MobicomWorkshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIALM), Dallas, December 16, 1998
] E. Amaldi, P. Belotti, A. Capone, F. Malucelli, Optimizing base station location and configuration in UMTS networks. Annals of Operations Research 146, 2006, 135–151.
A. Jedidi, A., Caminada, A., Finke, G., 2-Objective optimization of cells overlap and geometry with evolutionary algorithms. LectureNotes in Computer Science 3005, 2004, 130–139.
J. Zimmermann, R. Hons, H. Muhlenbein, ENCON: an evolutionary algorithm for the antenna placement problem. Computers & Industrial Engineering 44, 2003, 209–226
Y. T., Chan and K.C. Ho, A simple and Efficient Estimator for Hyperbolic Location, IEEE Transactions on Aerospace and Electronic Systems,42(8), 1994, p. 1905-1915.
R. Shimura and I. Sasase, TDOA mobile terminal positioning with weight control based on received power of pilot symbol in Taylor series estimation, 17th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2006, p. 1 - 5
Hao Li and M. Oussalah, Combination of Taylor and Chan method in Mobile Positioning, in: Proc. of IEEE CIS 2011 Conference, London, pp…
S. Venkatesh and R. M. Buehrer, “A linear programming approach to NLOS error mitigation in sensor networks,” in Proc. IEEE Int. Symp. Information Processing in Sensor Networks (IPSN), Nashville, Tennessee, Apr. 2006, pp. 301–308
Z. Li, W. Trappe, Y. Zhang, and B. Nath, “Robust statistical methods for securing wireless localization in sensor networks,” in Proc. IEEE Int. Symp. Information Processing in Sensor Networks (IPSN), Los Angeles, CA, Apr. 2005, pp. 91–98.
I. Guvenc, S. Gezici, F. Watanabe, and H. Inamura, “Enhancements to linear least squares localization through reference selection and ML estimation,” in Proc. IEEE Wireless Commun. Networking Conf. (WCNC), Las Vegas, NV, Apr. 2008, pp. 284–289
S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ: Prentice Hall, Inc., 1993
