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Spatiotemporal Location Fingerprint Generation Using Extended Signal Propagation Model
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 Title & Authors
Spatiotemporal Location Fingerprint Generation Using Extended Signal Propagation Model
Kim, Hee-Sung; Li, Binghao; Choi, Wan-Sik; Sung, Sang-Kyung; Lee, Hyung-Keun;
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 Abstract
Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.
 Keywords
Received signal strength;Signal propagation model;Fingerprinting;Positioning;
 Language
English
 Cited by
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A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics, Journal of Electrical Engineering and Technology, 2013, 8, 6, 1520  crossref(new windwow)
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Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks, International Journal of Distributed Sensor Networks, 2015, 2015, 1  crossref(new windwow)
 References
1.
Y. Chen and H. Kobayashi, "Signal strength based indoor geolocation," IEEE International Conference on Communications, pp. 436-439, 2002.

2.
Y. Wang, X. Jia, H. K. Lee, and G. Y. Li, "An indoor wireless positioning system based on wireless local area network," International Symposium on Satellite Navigation Technology Including Mobile Positioning and Location Services, paper 54, 2003.

3.
Y. Qi, Wireless Geolocation in a Non-Line-of-Sight Environment, Ph.D. dissertation, Princeton University, November 2003.

4.
A. Bahillo, S.Mazuelas, R. M. Lorenzo, P. Fernandez, J. Prieto, R. J. Duran, and E. J. Abril, "Hybrid RSSRTT Localization Scheme for Indoor Wireless Networks," EURASIP Journal on Advances in Signal Processing, Article ID 126082, 2010.

5.
P. Bahl, V. N. Padmanabhan, and A. Balachandran, A software system for locating mobile users: Design, evaluation, and lessons, Technical report, Microsoft Research, 2000.

6.
P. Bahl and V. N. Padmanabhan, "RADAR: An inbuilding RF-based user location and tracking system," IEEE INFOCOM, pp. 775-784, 2000.

7.
J. Krumm and J.C. Platt, Minimizing Calibration Effort for an In-door 802.11 Device Location Measurement System, Technical Report, Microsoft Research, 2003.

8.
B. Li, Y. Wang, H.K. Lee, A. Dempster and C. Rizos, "Method for yielding a database of location fingerprints in WLAN," IEE Proceedings-Communications, vol. 152, no. 5, pp. 580-586, 2005. crossref(new window)

9.
X. Chai and Q. Yang, "Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples," IEEE PerCom, 2005.

10.
M. Youssef and A. Agrawala, "Small-scale compensation for WLAN location determination systems," IEEE WCNC, pp. 1974-1978, 2003.

11.
S. Ganu, A. S. Krishnakumar, and P. Krishnan, "Infrastructure-based location estimation in WLAN networks," IEEE WCNC, pp. 465-470, 2004.

12.
H. Akima, "A new method of Interpolation and Smooth Curve Fitting based on Local Procedures," Journal of the ACM, vol. 17, no. 4, pp. 589-602, 1970. crossref(new window)

13.
Y. Gwon and R. Jain, "Error characteristics and calibration-free techniques for wireless LAN-based location estimation," ACM MobiWac, 2004.

14.
K. Kaemarungsi, Design of indoor positioning systems based on location fingerprinting technique, Ph.D. dissertation, University of. Pittsburgh, 2005.

15.
S. Pandey, B. Kim, F. Anjum, and P. Agrawal, "Client assisted location data acquisition scheme for secure enterprise wireless network," IEEE WCNC, pp. 1174-1179, 2005.

16.
H. Lim, L. C. Kung, J. C. Hou, and H. Luo, "Zeroconfiguration, robust indoor localization: theory and experimentation," IEEE INFOCOM, pp. 1-12, 2006.

17.
A. Kushki, K. N. Plataniotis, and A. N. Venetsanopoulos, "Kernel-Based Positioning in Wireless Local Area Networks," IEEE Tr. Mobile Computing, Vol. 6, No. 6, pp. 689-705, 2007 crossref(new window)

18.
H. K. Lee, J. Y. Shim, H. S. Kim, B. Li, and C. Rizos, "Feature Extraction and Spatial Interpolation for Improved Wireless Location Sensing", Sensors, Vol. 8, pp. 2865-2885, 2008 crossref(new window)

19.
N. A. C. Cressie, Statistics for spatial data. John Wiley & Sons, 1993.

20.
J. Lefebvre, H. Roussel, E. Walter, D. Lecointe, and W. Tabbara, "Prediction from wrong models: the Kriging approach," IEEE Antennas and Propagation Magazine, vol. 38, no. 4, pp. 35-45, 1996. crossref(new window)