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Localization error modeling of hybrid fingerprint-based techniques for indoor ultra-wideband systems

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Abstract

Acquiring an accurate estimate of a device’s indoor position can lead to the development of various services, such as disaster recovery, content delivery and efficient point-to-point communications. To this end, different positioning techniques have been developed aiming to minimize the localization error. UWB signals are well-suited to communication and tracking systems, as they provide increased tolerance to localization errors due to the fine time resolution of the transmitted pulses and their large bandwidth. This work presents two indoor Ultra-Wideband (UWB) positioning schemes based on the hybrid combination of Fingerprinting with temporal or angular data. The proposed schemes are evaluated using five different propagation models and different numbers of Anchor Nodes (AN) that emit omnidirectional UWB signals. Then, measures are gathered for these parameters and results are presented in terms of localization and angular error. Also, results are provided for the basic statistics, cumulative distribution function and the boxplot of the positioning error, thus providing probabilistic modeling for the localization error. The performance evaluation shows that the two proposed hybrid UWB schemes provide accurate indoor positioning independently of the type of data used during the localization procedure and optimum placement along with the optimum number of the ANs is provided.

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References

  1. Segou, O. E., & Thomopoulos, S. C. A. (2014). Probabilistic error modeling and topology-based smoothing of indoor localization and tracking data, based on the IEEE 802.15.4a chirp spread spectrum specification. Hindawi. International Journal of Distributed Sensor Networks, 2014, 310410. doi:10.1155/2014/310410.

    Article  Google Scholar 

  2. Milioris, D., Tzagkarakis, G., Papakonstantinou, A., Papadopouli, M., & Tsakalides, P. (2014). Low-dimensional signal-strength fingerprint-based positioning in wireless LANs. Elsevier of Ad-Hoc Networks, 12, 100–114.

    Article  Google Scholar 

  3. Yang, C., & Shao, H. R. (2015). WiFi-based indoor positioning. IEEE Communications Magazine, 53, 150–157.

    Article  Google Scholar 

  4. Misra, S., Kapri, N. R., & Wolfinger, B. E. (2015). Selfishness-aware target tracking in vehicular mobile WiMAX networks. Springer Telecommunication Systems Journal, 58(4), 313–328.

    Article  Google Scholar 

  5. Chowdhury, T. I., Rahman, M. M., Parvez, S. A., & Rizwan, S. (2015). A multi-step approach for RSSI-based distance estimation using smartphones. In IEEE international conference on networking systems and security (NSysS) (pp. 1–5).

  6. Huang, C. H., Lee, L. H., Ho, C. C., Wu, L. L., & Lai, Z. H. (2015). Real-time RFID indoor positioning system based on Kalman-filter drift removal and Heron-bilateration location estimation. IEEE Transactions on Instrumentation and Measurement, 64, 728–739.

    Article  Google Scholar 

  7. Mardeni, R., & Othman, S. N. (2015). Efficient mobile asset tracking and localization in ZigBee wireless network. Journal of Advances in Computer Networks (JACN), 3(1), 1–6.

    Article  Google Scholar 

  8. Vari, M. & Cassioli, D. (2014). mmWave RSSI indoor network localization, In IEEE international conference on communications (ICC) (pp. 127–132).

  9. Nardis, L. D., Panaitopol, J. F. D., & Di Benedetto, M. G. (2013). Combining UWB with time reversal for improved communication and positioning. Springer Telecommunication Systems Journal, 52(2), 1145–1158.

    Article  Google Scholar 

  10. Gezici, S., Tian, Z., Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., et al. (2005). Localization via ultra-wideband radios: A look at positioning aspects of future sensor networks. IEEE Signal Processing Magazine, 22(4), 70–84.

    Article  Google Scholar 

  11. Bogdani, E., Vouyioukas, D., Nomikos, N., Skoutas, D. N., & Skianis, C. (2014). A hybrid lateration time-fingerprint position estimation technique for indoor UWB systems. In IEEE international workshop on computer aided modeling of communication links and networks (CAMAD) (pp. 350–354).

  12. Bogdani, E., Vouyioukas, D., Nomikos, N., Skoutas, D. N., & Skianis, C. (2015). Angle-based time fingerprint positioning technique for indoor UWB systems. In IEEE international conference on communications (ICC).

  13. Gilski, P., & Stefanski, J. (2015). Survey of radio navigation systems. Jet International Journal of Electronics and Telecommunications, 61(1), 43–48.

    Google Scholar 

  14. Kim, J. H., Min, K. S., & Yeo, W. Y. (2014). A design of irregular grid map for large-scale WiFi LAN fingerprint positioning systems. Hindawi The ScientificWorldJournal, 2014, 203419. doi:10.1155/2014/203419.

    Google Scholar 

  15. Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, 37(6), 1067–1080.

    Article  Google Scholar 

  16. Meghani, S. K. & Asif, M. (2014). Localization of WSN node based on RTT ToA using ultra wideband & 802.1S.4a channel. In IEEE international conference on networking, sensing and control (ICNSC) (pp. 380–385).

  17. Xu, J., Ma, M., & Law, C. L. (2015). Cooperative angle-of-arrival position localization. Elsevier, Measurement, 59, 302–313.

    Article  Google Scholar 

  18. Subhan, F., Hasbullah, H., & Ashraf, K. (2013). Kalman filter-based hybrid indoor position estimation technique in bluetooth networks. Hindawi International Journal of Navigation and Observation, 2013, 570964. doi:10.1155/2013/570964.

    Google Scholar 

  19. Stoyanova, T., Kerasiotis, F., Prayati, A., & Papadopoulos, G. (2009). Evaluation of impact factors on RSS accuracy for localization and tracking applications in sensor networks. Springer Telecommunication Systems Journal, 42(3–4), 235–248.

    Article  Google Scholar 

  20. He, T., Huang, C., Blum, B. M., Stankovic, J. S., & Abdelzahe, T. F. (2005). Range-free localization and its impact on large scale sensor networks. ACM Transactions on Embedded Computing Systems, 4, 877–906.

    Article  Google Scholar 

  21. Sangthong, Z. J., Dokpikul, P., & Promwong, S. (2012). Indoor positioning based on IEEE 802.15.4a standard using trilateration technique and UWB signal. In Proceedings in progress in electromagnetics research symposium.

  22. Onalaja, O., Adjrad, M., & Ghavami, M. (2014). Ultra-wideband-based multilateration technique for indoor localization. IET Communications, 8(10), 1800–1809.

    Article  Google Scholar 

  23. Farid, Z., Nordin, R., & Ismail, M. (2013). Recent advances in wireless indoor localization techniques and system. Hindawi Journal of Computer Networks and Communications, 2013, 12.

    Google Scholar 

  24. Kaemarungsi, K. & Krishnamurthy, P. (2004). Modeling of indoor positioning systems based on location fingerprinting. In IEEE conference of computer and communications society (INFOCOM) (Vol. 2, pp. 1012–1022).

  25. Yassin, M., Rachid, E., & Nasrallah, R. (2013). Performance comparison of positioning techniques in WiFi networks. In IEEE international conference on innovations in information technology (INNOVATIONS) (pp. 75–79).

  26. Xiao, T. T., Liao, X. Y., Hu, K., & Yu, M. (2014). Study of fingerprint location algorithm based on WiFi technology for indoor localization. In IET international conference on wireless communications, networking and mobile computing (WiCOM) (pp. 604–608).

  27. Alhmiedat, T., Samara, G., Ho, C. C., & Salem, A. O. A. (2013). An indoor Fingerprinting localization approach for ZigBee wireless sensor networks. European Journal of Scientific Research, 105(2), 190–202.

    Google Scholar 

  28. Vinicchayakul, W. & Promwong, S. (2014). Improvement of fingerprinting technique for UWB indoor localization. In IEEE joint international conference on information and communication technology, electronic and electrical engineering (JICTEE) (pp. 1–5).

  29. Kabir, H., & Kohno, R. (2012). A hybrid TOA-Fingerprinting based localization of mobile nodes using UWB signaling for non line-of-sight condition. MDPI Open Access Journal of Sensors, 12(8), 11187–11204.

    Article  Google Scholar 

  30. Wang, T., Chent, X., Ge, N., & Pei, Y. (2013). Error analysis and experimental study on indoor UWB TDoA localization with reference tag. In IEEE Asia-Pacific conference on communications (APCC) (pp. 505–508).

  31. Liu, X., Zhang, S., & Bu, K. (2015). A locality-based range-free localization algorithm for anisotropic wireless sensor networks. Telecommunication Systems. doi:10.1007/s11235-015-9978-8.

  32. Brida, P., Machaj, J., & Benikovsky, J. (2014). Wireless sensor localization using enhanced DV-AoA algorithm. TUBITAK Turkish Journal of Electrical Engineering & Computer Sciences, 22(3), 679–689.

    Article  Google Scholar 

  33. Horiba, M., Okamoto, E., Shinohara, T., & Matsumura, K. (2013). An improved NLOS detection scheme for hybrid-TOA/AOA-based localization in indoor environments. In IEEE international conference on UWB (ICUWB) (pp. 37-42).

  34. Peng, R. & Sichitiu, M. L. (2006). Angle of arrival localization for wireless sensor networks. IEEE communication society on sensors and ad-hoc communication and networks (SECON) (pp. 374–378).

  35. MacCartney Jr., G. R., Samimi, M. K., & Rappaport, T. S. (2015). Exploiting directionality for millimeter-wave wireless system improvement. In 2015 IEEE international conference on communications (ICC).

  36. Malajner, M., Planinsic, P., & Gleich, D. (2012). Angle of arrival estimation using RSSI and omnidirectional rotatable antennas. IEEE Sensors Journal, 12(6), 1950–1957.

    Article  Google Scholar 

  37. Gvozdenovic, N. & Eric, M. (2011). Localization of users in multiuser MB OFDM UWB systems based on TDoA principle. In IEEE telecommunication forum (TELFOR) (pp. 326–329).

  38. Standard ECMA-368—High rate ultra-wideband PHY and MAC standard. Ecma Int. Std.

  39. Abhayawardhana, V. S., Wassell, I. J., Crosby, D., Sellars, M. P., & Brown, M. G. (2005). Comparison of empirical propagation path loss models for fixed wireless access systems. IEEE VTC, 1, 73–77.

    Google Scholar 

  40. Kumar, S., Gupta, P. K., Singh, G., & Chauhan, D. S. (2013). Performance analysis of Rayleigh and Rician fading channel models using Matlab simulation. International Journal of Intelligent Systems and Applications, 9, 94–102.

    Article  Google Scholar 

  41. Federal Communications Commission (FCC). Revision of part 15 of the commission’s rules regarding ultra-wideband transmission systems: First report and order. Technical Report FCC 02-48, 2002.

  42. Thas, O. (2010). Comparing distributions., Springer Series in Statistics New York: Springer. ISBN: 978-0-387-92710-7.

    Book  Google Scholar 

  43. Rappaport, T. S., Siedel, S. Y., & Takamizawa, K. (1991). Statistical channel impulse response models for factory and open plan building radio communication system design. IEEE Transactions on Communications, 39(5), 794–807.

    Article  Google Scholar 

  44. De Adana, F. S. (2011). Propagation models for the characterization of the indoor UWB channel, ultra wideband communications: Novel trends—Antennas and propagation. InTech. ISBN: 978-953-307-452-8. doi:10.5772/20171.

  45. Malik, W. Q., Allen, B., & Edwards, D. J. (2008). Bandwidth-dependent modelling of small-scale fade depth in wireless channels. IET Microwaves, Antennas and Propagation, 2, 519–528.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovassi, Samos, Greece

    Eleni Bogdani, Demosthenes Vouyioukas & Nikolaos Nomikos

Authors
  1. Eleni Bogdani
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  2. Demosthenes Vouyioukas
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  3. Nikolaos Nomikos
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Correspondence to Demosthenes Vouyioukas.

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Bogdani, E., Vouyioukas, D. & Nomikos, N. Localization error modeling of hybrid fingerprint-based techniques for indoor ultra-wideband systems. Telecommun Syst 63, 223–241 (2016). https://doi.org/10.1007/s11235-015-0116-4

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