Power strength or Received Signal Strength Indicator (RSSI), a primary technique used in Real Time Location Systems (RTLS), is analyzed in this paper for RFID tracking applications. Critical issues are studied and hardware novelties are introduced in order to improve its performance. The main novelty is the accomplishment of an RFID RTLS through a mesh of individual active radiofrequency (RF) barriers composed by active emitter and receiver nodes/tags that cover only small individual areas. The result is a Sensor Area Network (SAN) that offers some advantages over classical tracking systems, which are based on Wireless Sensor Networks (WSN), especially in the multipath impairment mitigation, such as a controlled power emission, and the chance to warrant privacy regarding the exchange of RFID information. Experimental measurements were done to estimate the influence of the transmitted signal type and the receiver end architecture in the detection of the RF barrier presence. The parameterization of the coverage area of a SAN cell in terms of power is derived for both free-space and log-distance propagation models. The Kalman filtering technique is introduced as a valid tool to severely mitigate the multipath propagation effects that can affect the accurate operation of the proposed SAN for indoor operation conditions. Outcomes show a promising performance for this wireless network design, which has not received enough attention in literature.
1. Liu, H.-Q. and H.-C. So, "Target tracking with line-of-sight identification in sensor networks under unknown measurement noises," Progress In Electromagnetics Research, Vol. 97, 373-389, 2009. doi:10.2528/PIER09090701
2. Liu, H.-Q., H.-C. So, K. W. K. Lui, and F. K. W. Chan, "Sensor selection for target tracking in sensor networks," Progress In Electromagnetics Research, Vol. 95, 267-282, 2009. doi:10.2528/PIER09070802
3. Kuo, S.-K., J.-Y. Hsu, and Y.-H. Hung, "Analysis and design of an UHF RFID metal tag using magnetic composite material as substrate," Progress In Electromagnetics Research B, Vol. 24, 49-62, 2010. doi:10.2528/PIERB10070107
4. Loo, C.-H., A. Z. Elsherbeni, F. Yang, and D. Kajfez, "Experimental and simulation investigation of RFID blind spots," Journal of Electromagnetic Waves and Applications, Vol. 23, No. 5-6, 747-760, 2009. doi:10.1163/156939309788019750
5. Geok, T. K., A. W. Reza, and C.-P. Tan, "Objects tracking utilizing square grid RFID reader antenna network," Journal of Electromagnetic Waves and Applications, Vol. 22, No. 1, 27-38, 2008. doi:10.1163/156939308783122724
6. Lipsky, S. E., Microwave Passive Direction Finding, Wiley Interscience, 1987.
7. Poor, H., An Introduction to Signal Detection and Estimation, Ch. 4, New York, Springer-Verlag, 1985.
8. Liao, B., G. Liao, and J. Wen, "A method for DOA estimation in the presence of unknown non uniform noise," Journal of Electromagnetic Waves and Applications, Vol. 22, No. 14-15, 2113-2123, 2008. doi:10.1163/156939308787537856
9. Gomez, J., A. Tayebi, F. Saez de Adana, and O. Gutierrez, "Localization approach based on ray-tracing including the effect of human shadowing," Progress In Electromagnetics Research Letters, Vol. 15, 1-11, 2010. doi:10.2528/PIERL10030908
10. Zhang, X., G. Feng, and D. Xu, "Blind direction of angle and time delay estimation algorithm for uniform linear array employing multi-invariance music," Progress In Electromagnetics Research Letters, Vol. 13, 11-20, 2010. doi:10.2528/PIERL09102611
11. Rappaport, T. S., J. H. Reed, and B. D. Woerner, "Position location using wireless communications on highways of the future," IEEE Communications Magazine, Vol. 34, No. 10, 33-41, 1996. doi:10.1109/35.544321
12. Boukerche, A., H. A. B. Oliveira, E. F. Nakamura, and A. A. F. Loureiro, "Localization systems for wireless sensor networks," IEEE Wireless Communications, Vol. 14, No. 6, 6-12, 2007. doi:10.1109/MWC.2007.4407221
13. Benkic, K., M. Malajner, P. Planinšic, and Ž. Cucej, "Using RSSI value for distance estimation in wireless sensor networks based on ZigBee," IEEE International Conference on Pervasive Computing and Communications, 303-306, Galveston, Texas, USA, March 2009.
14. Zhao, J., Y. Zhang, and M. Ye, "Research on the received signal strength indication location algorithm for RFID system," Proceedings of the International Symposium on Communications and Information Technologies, 881-885, Bangkok, October 2006.
15. Kaemarungsi, K. and P. Krishnamurthy, "Properties of indoor received signal strength for WLAN location fingerprinting," Proceedings of the MobiQuitous, 14-23, Boston, Massachusetts, USA, August 22-26, 2004.
16. Kaemarungsi, K., "Distribution of WLAN received signal strength indication for indoor location determination," IEEE International Symposium on Wireless Pervasive Computing, 1-4, Phuket, Thailand, January 16-18, 2006.
17. Corbley, K. P., "WiFi inadequate as real-time asset and patient tracking solution," Wireless Design Magazine, 30-31, November 2008.
18., "Sensor area network for integrated systems health management,", NASA SBIR contract NNX08CC91P, 2007.
19. Yu, G.-J., "An efficient storage policy for moving target path extraction in wireless sensor networks," IET Communications, Vol. 3, No. 9, 1488-1497, 2009. doi:10.1049/iet-com.2008.0431
20. Simon, D., Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches, John Wiley & Sons, London, 2006. doi:10.1002/0470045345
21. Jiang, T., N. D. Sidiropoulos, and G. B. Giannakis, "Kalman filtering for power estimation in mobile communications," IEEE Trans. Wireless Communications, Vol. 2, No. 1, 151-161, 2003. doi:10.1109/TWC.2002.806386
22. Ahson, S. A. and M. Ilyas, RFID Handbook: Applications, Technology, Security, and Privacy, 1 Ed., CRC Press, 2008. doi:10.1201/9781420055009