Mitigation of the Ground Reflection Effect in Real-Time Locating Systems
- 757 Downloads
Abstract
Real-Time Locating Systems (RTLS) are one of the most promising applications based on Wireless Sensor Networks and represent a currently growing market. However, accuracy in indoor RTLS is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radio frequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath and the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLS, focusing on the mitigation of the ground reflection effect by using Artificial Neural Networks.
Keywords
Wireless Sensor Networks Real-Time Locating Systems Ground Reflection Effect Artificial Neural NetworksPreview
Unable to display preview. Download preview PDF.
References
- 1.Barclay, L.W., I.O.E. Engineers.: Propagation of Radiowaves. Iet (2003) Google Scholar
- 2.Kim, E.S., Kim, J.I., Kang, I.-S., Park, C.G., Lee, J.G.: Simulation Results of Ranging Performance in Two-Ray Multipath Model. In: International Conference on Control, Automation and Systems, ICCAS 2008, pp. 734–737 (2008)Google Scholar
- 3.Goldsmith, A.: Wireless Communications. Cambridge University Press, Cambridge (2005)Google Scholar
- 4.Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions On Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(6), 1067–1080 (2007)CrossRefGoogle Scholar
- 5.Kaemarungsi, K., Krishnamurthy, P.: Modeling Of Indoor Positioning Systems Based On Location Fingerprinting. In: Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004, vol. 2, pp. 1012–1022 (2004)Google Scholar
- 6.Katsuura, H., Sprecher, D.: Computational Aspects of Kolmogorov’s Superposition Theorem. Neural Networks 7(3), 455–461 (1994)CrossRefzbMATHGoogle Scholar
- 7.Lecun, Y., Bottou, L., Orr, G.B., Müller, K.R.: Efficient Backprop. LNCS, pp. 5–50. Springer, Heidelberg (1998)Google Scholar
- 8.N-Core, N-Core: A Faster and Easier Way to Create Wireless Sensor Networks (2010), http://Www.N-Core.Info (retrieved October 27, 2010)
- 9.Nerguizian, C., Despins, C., Affès, S.: Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks. In: de Souza, J.N., Dini, P., Lorenz, P. (eds.) ICT 2004. LNCS, vol. 3124, pp. 866–875. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 10.Nguyen, H., Chan, C.: Multiple Neural Networks for a Long Term Time Series Forecast. Neural Computing & Applications 13(1), 90–98 (2004)CrossRefGoogle Scholar
- 11.Ray, J.K., Cannon, M.E., Fenton, P.C.: Mitigation Of Static Carrier-Phase Multipath Effects Using Multiple Closely Spaced Antennas. Navigation-Washington 46(3), 193–202 (1999)Google Scholar
- 12.Salcic, Z., Chan, E.: Mobile Station Positioning Using GSM Cellular Phone and Artificial Neural Networks. Wireless Personal Communications 14(3), 235–254 (2000)CrossRefGoogle Scholar
- 13.Schmitz, A., Wenig, M.: The Effect of the Radio Wave Propagation Model in Mobile Ad Hoc Networks. In: Proceedings of the 9th ACM International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems, Terromolinos, Spain, pp. 61–67 (2006)Google Scholar
- 14.Tapia, D.I., De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Multi-Agent System For Security Control On Industrial Environments. International Transactions on System Science and Applications Journal 4(3), 222–226 (2008)Google Scholar
- 15.Vapnik, V.N.: Statistical Learning Theory. Wiley Interscience, Hoboken (1998)zbMATHGoogle Scholar
- 16.Xie, J.J., Palmer, R., Wild, D.: Multipath Mitigation Technique in RF Ranging. In: Canadian Conference on Electrical and Computer Engineering, pp. 2139–2142 (2005)Google Scholar