Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Indoor Localization based on Multiple Neural Networks

다중 인공신경망 기반의 실내 위치 추정 기법

  • Sohn, Insoo (Division of Electronics & Electrical Engineering, Dongguk University)
  • 손인수 (동국대학교 전자전기공학부)
  • Received : 2014.11.28
  • Accepted : 2015.01.27
  • Published : 2015.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Indoor localization is becoming one of the most important technologies for smart mobile applications with different requirements from conventional outdoor location estimation algorithms. Fingerprinting location estimation techniques based on neural networks have gained increasing attention from academia due to their good generalization properties. In this paper, we propose a novel location estimation algorithm based on an ensemble of multiple neural networks. The neural network ensemble has drawn much attention in various areas where one neural network fails to resolve and classify the given data due to its' inaccuracy, incompleteness, and ambiguity. To the best of our knowledge, this work is the first to enhance the location estimation accuracy in indoor wireless environments based on a neural network ensemble using fingerprinting training data. To evaluate the effectiveness of our proposed location estimation method, we conduct the numerical experiments using the TGn channel model that was developed by the 802.11n task group for evaluating high capacity WLAN technologies in indoor environments with multiple transmit and multiple receive antennas. The numerical results show that the proposed method based on the NNE technique outperforms the conventional methods and achieves very accurate estimation results even in environments with a low number of APs.

Keywords

References

  1. K. Pahlavan, X. Li, and J. Makela, "Indoor geolocation science and technology," IEEE Communications Magazine, vol. 40, no. 2, pp. 112-118, 2002. https://doi.org/10.1109/35.983917
  2. H. Liu, H. Darabi, P. Banerjee, and P. Liu, "Survey of wireless indoor positioning techniques and systems," IEEE Transactions on Systems, Man, and Cybernetics, vol. 37, no. 6, pp. 1067-1080, 2007. https://doi.org/10.1109/TSMCC.2007.905750
  3. S. Tekinay, "Wireless geolocation systems and services," IEEE Communications Magazine, vol. 36, no. 5, p. 28, 1998.
  4. A. H. Sayed, A. Tarighat, and N. Khajehnouri, "Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information," IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 24-40, 2005. https://doi.org/10.1109/MSP.2005.1458275
  5. H. Akcan and C. Evrendilek, "GPS-free directional localization via dual wireless radios," Computer Communications, vol. 35, no. 9, pp. 1151-1163, 2012. https://doi.org/10.1016/j.comcom.2011.09.006
  6. P. Almers, E. Bonek, and A. Burr, et al., "Survey of channel and radio propagation models for wireless MIMO systems," EURASIP Journal on Wireless Communications and Networking, vol. 2007, pp. 1-20, 2007.
  7. H.-M. Lee and D.-S. Kim, "Insect-inspired algorithm for zone radius determination of ad-hoc networks," Journal of Institute of Control, Robotics, and Systems (in Korean), vol. 20, no. 10, pp. 1079-1083, 2014. https://doi.org/10.5302/J.ICROS.2014.14.8015
  8. Y. Gu, A. Lo, and I. Niemegeers, "A survey of indoor positioning systems for wireless personal networks," IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 13-32, 2009. https://doi.org/10.1109/SURV.2009.090103
  9. T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, "A probabilistic approach to WLAN user location estimation," International Journal of Wireless Information Networks, vol. 9, no. 3, pp. 155-164, 2002. https://doi.org/10.1023/A:1016003126882
  10. G. Deak, K. Curran, and J. Condell, "A survey of active and passive indoor localisation systems," Computer Communications, vol. 35, no. 16, pp. 1939-1954, 2012. https://doi.org/10.1016/j.comcom.2012.06.004
  11. S. Y. Cho and J. G. Park, "Radio propagation model an spatial correlation method-based efficient database construction for positioning fingerprints," Journal of Institute of Control, Robotics, and Systems (in Korean), vol. 20, no. 7, pp. 774-781, 2014. https://doi.org/10.5302/J.ICROS.2014.14.0010
  12. P. Bahl and V. N. Padmanabhan, "RADAR: An in-building RFbased user location and tracking system," Proc. of the IEEE INFOCOM, pp. 775-784, Mar. 2000.
  13. E. Elnahrawy, X. Li, and R. P. Martin, "The limits of localization using signal strength: a comparative study," Proc. of the IEEE SECON, pp. 406-414, Oct. 2004.
  14. K. Kleisouris, Y. Chen, J. Yang, and R. P. Martin, "Empirical evaluation of wireless localization when using multiple antennas," IEEE Trans. Parallel and Distributed Systems, vol. 21, no. 11, pp. 1595-1610, 2010. https://doi.org/10.1109/TPDS.2010.39
  15. D. Madigan, E. Elnahrawy, R. P. Martin, W. Ju, P. Krishnan, and A. Krishnakumar, "Bayesian indoor positioning systems," Proceeding of the IEEE INFOCOM, pp. 1217-1227, Mar. 2005.
  16. G. Chandarasekaran, M. Ergin, J. Yang, S. Liu, Y. Chen, M. Gruteser, and R. Martin, "Empirical evaluation of the limits on localization using signal strength," Proc. of the IEEE SECON, pp. 1-9, Jun. 2009.
  17. S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan, 1994.
  18. S. Merigeault, M. Batariere, and J. N. Patillon, "Data fusion based on neural network for the mobile subscriber location," Proc. of the IEEE VTC, pp. 536-541, Sep. 2000.
  19. H. Zamiri-Jafarian, M. M. Mirsalehi, I. Ahadi-Akhlaghi, and H. Keshavarz, "A neural network-based mobile positioning with hierarchical structure," Proc. of the IEEE VTC, pp. 2003-2007, Apr. 2003.
  20. C. Nerguizian, C. Despins, S. Affes, G. I. Wassi, and D. Grenier, "Neural network and fingerprinting-based geolocation on timevarying channels," Proc. of the IEEE PIMRC, pp. 1-6, Sep. 2006.
  21. D. S. Broomhead and D. Lowe, "Multivariable functional interpolation and adaptive networks," Complex Syst., vol. 2, pp. 321-355, 1988.
  22. H. Zamiri-Jafarian, M. M. Mirsalehi, I. Ahadi-Akhlaghi, and K. N. Plataniotis, "Mobile station positioning using radial basis function networks," Proc. of the IEEE PIMRC, pp. 2797-2800, Sep. 2004.
  23. C. Laoudis, P. Kemppi, and C. G. Panayiotou, "Localization using radial basis function networks and signal strength fingerprints in WLAN," Proc. of the IEEE GLOBECOM, pp. 1-6, Nov. 2009.
  24. L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-1001, 1990. https://doi.org/10.1109/34.58871
  25. A. Krogh and J. Vedelsby, "Neural network ensembles, cross validation, and active learning," Advances in Neural Information Processing Systems 7, pp. 231-238, 1995.
  26. D. Jimenez, "Dynamically weighted ensemble neural network for classification," Proc. of the IEEE World Congress on Computational Intelligence, vol. 1, pp. 753-756, Jul. 1998.
  27. TGn Channel Models, IEEE Std. 802.11-03/940r4, May 2004.
  28. E. Perahia, "IEEE 802.11n development: history, process, and technology," IEEE Commun. Mag., vol. 46, no. 7, pp. 48-55, 2008. https://doi.org/10.1109/MCOM.2008.4557042
  29. T. K. Paul and T. Ogunfunmi, "Wireless LAN comes of age: understanding the IEEE 802.11n amendment," IEEE Circuits and Systems Mag., vol. 8, no. 1, pp. 28-54, 2008. https://doi.org/10.1109/MCAS.2008.915504
  30. L. Schumacher and B. Kijkstra, Description of a MATLAB Implementation of the Indoor MIMO WLAN Channel Model Proposed by the IEEE 802.11 TGn, May 2004.
  31. I. Sohn and N. Ansari, "Configuring RBF neural networks," Electronics Letters, vol. 34, no. 7, pp. 684-685, 1998. https://doi.org/10.1049/el:19980469
  32. J. Torres-Sospedra, C. Hernandez-Espinosa, and M. Fernandez-Redondo, "A comparison of combination methods for ensembles of RBF networks," Proc. of the IEEE IJCNN, pp. 1137-1141, Jul.-Aug. 2005.

Cited by

  1. Reconstructing Damaged Complex Networks Based on Neural Networks vol.9, pp.12, 2017, https://doi.org/10.3390/sym9120310