Skip to main content
SpringerLink
Log in

Use of negative information in positioning and tracking algorithms

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

To avoid additional hardware deployment, indoor localization systems have to be designed in such a way that they rely on existing infrastructure only. Besides the processing of measurements between nodes, localization procedure can include the information of all available environment information. In order to enhance the performance of Wi-Fi based localization systems, the innovative solution presented in this paper considers also the negative information. An indoor tracking method inspired by Kalman filtering is also proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Want, R., Hopper, A., Falcao, V., & Gibbons, J. (1992). The active badge location system. ACM Transactions on Information Systems, 10(1), 91–102.

    Article  Google Scholar 

  2. Bahl, P., & Padmanabhan, V. N. (2000). RADAR: an in-building RF-based user location and tracking system. In Proc. INFOCOM 2000, Tel Aviv, Israel, March 2000 (Vol. 2, pp. 775–784).

    Google Scholar 

  3. Irfan, N., Bolic, M., Yagoub, M., & Narashiman, V. (2010). Localization of sensors in indoor environment with neural network method. Telecommunications Systems, 44(1), 149–158.

    Article  Google Scholar 

  4. LaMarca, A., Chawathe, Y., Consolvo, S., et al. (2005). Place lab: device positioning using radio beacons in the wild. In Proc. IEEE international conference on pervasive computing and communications (Vol. 3468, pp. 116–133). Berlin: Springer.

    Google Scholar 

  5. De Luca, D., Mazzenga, F., Monti, C., & Vari, M. (2006). Performance evaluation of indoor localization techniques based on RF power measurements from active or passive devices. EURASIP Journal on Applied Signal Processing, 2006(1), 1–11.

    Google Scholar 

  6. Priyantha, N. B., Chakraborty, A., & Balakrishnan, H. (2000). The Cricket location-support system. In International conference on mobile computing and networking, Boston, Massachusetts, USA (pp. 32–43).

    Google Scholar 

  7. Han, G., Xu, H., Duong, T., Jiang, J., & Hara, T. (2011). Localization algorithms of wireless sensor networks: a survey. Telecommunications Systems. doi:10.1007/s11235-011-9564-7. Preview published online on 4 August 2011.

    Google Scholar 

  8. Wu, C., Sheng, W., & Zhang, Y. (2006). Mobile self-localization using multi-dimensional scaling in robotic sensor networks. The International Journal of Intelligent Control and Systems, 11(3), 163–175.

    Google Scholar 

  9. Costa, J., Patwari, N., & Hero, A. (2006). Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Transactions on Sensor Networks, 2(1), 39–64.

    Article  Google Scholar 

  10. Doherty, L., Pister, K. S. J., & Ghaoui, L. E. (2001). Convex position estimation in wireless sensor networks. In Proc. of IEEE INFOCOM, April 2001 (Vol. 3, pp. 1655–1663).

    Google Scholar 

  11. Biswas, P., & Ye, Y. (2004). Semidefinite programming for ad hoc wireless sensor network localization. In Proc. international conference on information processing in sensor networks (IPSN ’04), Berkeley, CA, April 2004.

    Google Scholar 

  12. Savvides, A., Han, C., & Srivastava, M. (2001). Dynamic fine-grained localization in ad-hoc networks of sensors. In 7th ACM international conference on mobile computing and networking (Mobicom), Rome, Italy, July 2001 (pp. 166–179).

    Google Scholar 

  13. Aslindi, N., Pahlavan, K., Alavi, B., & Li, X. (2006). A novel cooperative localization algorithm for indoor sensor networks. In IEEE international symposium on personal, indoor and mobile radio communications PIMRC ’06, Helsinki, Finland (pp. 1–6).

    Google Scholar 

  14. Liu, J., & Zhang, Y. (2008). Error control in distributed node self-localization. EURASIP Journal on Advances in Signal Processing, January.

  15. Ihler, A. T., Fisher, J. W., & Moses, R. L. (2004). Nonparametric belief propagation for self-calibration in sensor networks. In Proceedings of the 3rd international symposium on information processing in sensor networks, Berkeley, CA, USA (pp. 225–233).

    Chapter  Google Scholar 

  16. Savic, V., & Zazo, S. (2009). Nonparametric boxed belief propagation for localization in wireless sensor networks. In Proceedings of IEEE SENSORCOMM, Athens, Greece, June 2009 (pp. 520–525).

    Google Scholar 

  17. Savic, V., & Zazo, S. (2009). Sensor localization using nonparametric generalized belief propagation in network with loops. In Proceedings of IEEE international conference on information fusion, Seattle, USA, July 2009 (pp. 1966–1973).

    Google Scholar 

  18. Wymeersch, H., Lien, J., & Win, M. Z. (2009). Cooperative localization in wireless networks. Proceedings of the IEEE, 97(2). Special Issue on UWB Technology and Emerging Applications.

  19. Dellaert, F., et al. (1999). Monte Carlo localization for mobile robots. In IEEE international conference on robotics and automation (ICRA), May 1999.

    Google Scholar 

  20. Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proceedings of ACM MobiCom 2004, Philadelphia, USA, September 2004 (pp. 45–57).

    Google Scholar 

  21. Hoffmann, J., Spranger, M., & Gohring, D. (2005). Making use of what you don’t see: negative information in Markov localization. In Proc. of IEEE/RSJ international conference of intelligent robots and systems (IROS), Alberta, Canada, August 2005.

    Google Scholar 

  22. Koch, W. (2004). On negative information in tracking and sensor data fusion: discussion of selected examples. In Proc. of 7th international conference on information fusion (pp. 91–98).

    Google Scholar 

  23. Montemerlo, M., & Thrun, S. (2003). Simultaneous localization and mapping with unknown data association using FastSLAM. In Proc. IEEE international conference on robotics and automation (pp. 1985–1991).

    Google Scholar 

  24. Baggio, A., & Langendoen, K. (2008). Monte-Carlo localization for mobile wireless sensor networks. Ad Hoc Networks, 6(5), 718–733.

    Article  Google Scholar 

  25. Caceres, M. A., Sottile, F., & Spirito, M. A. (2009). Adaptive location tracking by Kalman filters in wireless sensor networks. In Proc. of IEEE international conference on wireless and mobile computing, networking and communications.

    Google Scholar 

  26. Welch, G. F., & Bishop, G. (2006). An introduction to the Kalman filter. (Tech. Rep. 95-041). University of North Carolina, Chapel Hill, NC, USA. Updated July 2006.

  27. Sir Conan Doyle, A. (1890). The sign of the four.

  28. Rappaport, T. S. (2002). Wireless communications principles and practices. New York: Prentice-Hall.

    Google Scholar 

  29. Akl, R., Tummala, D., & Li, X. (2006). Indoor propagation modeling at 2.4 GHz for IEEE 802.11 networks. In Proc. 6th IASTED international multi-conference on wireless and optical communications, Banff.

    Google Scholar 

  30. Sadiki, T., & Paimblanc, P. (2009). Modelling new indoor propagation models for WLAN based on empirical results. In Proc. UKSim 2009: 11th international conference on computer modelling and simulation, Cambridge, England, March 2009.

    Google Scholar 

  31. Stuedi, P., Chinellato, O., & Alonso, G. (2005). Connectivity in the presence of shadowing in 802.11 ad hoc networks. In Proc. IEEE wireless communications and networking conference (WCNC), Mar. 2005 (pp. 2225–2230).

    Google Scholar 

  32. http://wifihopper.com/.

  33. Goldsmith, A. (2005). Wireless communications, ISBN 9780521837163, September 2005

  34. Mathur, R., Klepal, M., McGibney, A., & Pesch, D. (2004). Influence of people shadowing on bit error rate of IEEE 802.11 2.4 GHz channel. In 1st International symposium on wireless communication systems (ISWCS 2004), Port-Louis, Mauritius, September 2004 (pp. 448–452).

    Google Scholar 

  35. Chrysikos, T., Georgopoulos, G., & Kotsopoulos, S. (2010). Impact of shadowing deviation on wireless channel characterization for a public indoor commercial topology at 2.4 GHz. In 2nd International congress on ultra modern telecommunications (ICUMT 2010), Moscow, Russia, 18–20 October 2010 (pp. 18–20).

    Google Scholar 

  36. Butterworth, K. S., Sowerby, K. W., & Williamson, A. G. (2000). Base station placement for in-building mobile communication systems to yield high capacity and efficiency. IEEE Transactions on Communications, 48(4), 658–669.

    Article  Google Scholar 

  37. Agrawal, P., & Patwari, N. (2009). Correlated link shadow fading in multi-hop wireless networks. IEEE Transactions on Wireless Communications, 8(8), 4024–4036.

    Article  Google Scholar 

Download references

Acknowledgements

This work has been performed in the framework of the ICT project ICT-248894 WHERE2, which is partly funded by the European Union.

Author information

Authors and Affiliations

  1. CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto R. Dr. António Bernardino de Almeida, 431 4200-072, Porto, Portugal

    Michele Albano

  2. Instituto de Telecomunicações (IT—Aveiro), Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

    Senka Hadzic & Jonathan Rodriguez

Authors
  1. Michele Albano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Senka Hadzic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michele Albano.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Albano, M., Hadzic, S. & Rodriguez, J. Use of negative information in positioning and tracking algorithms. Telecommun Syst 53, 285–298 (2013). https://doi.org/10.1007/s11235-013-9698-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-013-9698-x

Keywords

Search

Navigation