Advertisement

Wireless Networks

, Volume 25, Issue 1, pp 215–227 | Cite as

A geometrical closed form solution for RSS based far-field localization: Direction of Exponent Uncertainty

  • Seçkin UluskanEmail author
  • Tansu Filik
Article
  • 193 Downloads

Abstract

In this study, a new powerful geometrical closed-form solution called Direction of Exponent Uncertainty (DEU) is proposed for received signal strength (RSS) based far-field localization when path loss exponent (PLE) and transmit power are both unknown. The uncertainty in the PLE due to environmental factors is a significant challenge for RSS based localization. DEU is built after careful investigation of geometrical behaviors of differential received signal strength circles, i.e. the locus of possible location of the emitter when transmit power is unknown. It is shown that the uncertainty in the PLE corresponds to a linear uncertainty for the location of the emitter in two dimensional space. This critical observation creates a basis for the sensor to move towards the emitter without estimating the emitter location after only three measurements. Furthermore, with only four different measurements, it is possible to effectively estimate the location of the emitter as well as the PLE by means of intersection of DEUs. Intersection of DEUs attains Cramer Rao Lower Bound with a dramatically reduced execution time compared to nonlinear least squares estimator. DEU is also proposed as an efficient route planning tool for moving sensors such as unmanned aerial vehicles.

Keywords

Far-field localization Geometrical solution Received signal strength Unknown path loss exponent Emitter tracking 

Notes

Acknowledgements

This study is funded by TUBITAK (The Scientific and Technological Research Council of Turkey) with the project number 115E185 and by Anadolu University with the project number 1606F559.

Supplementary material

Video 1. A descriptive video about DEU based tracking. https://youtu.be/nGyzCvXR8SM. (AVI 27362 kb)

Video 2. An illustrative sample video about the emitter tracking simulations. https://youtu.be/NF26-Y6C_q4. (MP4 1240 kb)

References

  1. 1.
    Gezici, S. (2008). A survey on wireless position estimation. Wireless Personal Communications, 44(3), 263–282.CrossRefGoogle Scholar
  2. 2.
    Zekavat, R., & Buehrer, R. M. (2011). Handbook of position location: Theory, practice and advances. Singapore: Wiley.CrossRefGoogle Scholar
  3. 3.
    Jin, R., Che, Z., Xu, H., Wang, Z., & Wang, L. (2015). An RSSI-based localization algorithm for outliers suppression in wireless sensor networks. Wireless Networks, 21(8), 2561–2569.CrossRefGoogle Scholar
  4. 4.
    Ren, L., Chen, X., Xie, B., Tang, Z., Xing, T., Liu, C., et al. (2016). DE2: Localization based on the rotating RSS using a single beacon. Wireless Networks, 22(2), 703–721.CrossRefGoogle Scholar
  5. 5.
    Liu, C., Wu, K., & He, T. (2004). Sensor localization with ring overlapping based on comparison of received signal strength indicator. In IEEE international conference on mobile ad hoc and sensor systems (pp. 516–518).Google Scholar
  6. 6.
    Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., III, Moses, R. L., & Correal, N. S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. Signal Processing Magazine, 22(4), 54–69.CrossRefGoogle Scholar
  7. 7.
    Wang, G., & Yang, K. (2009). Efficient semidefinite relaxation for energy-based source localization in sensor networks. In IEEE international conference on acoustics, speech and signal processing (ICASSP 2009) (pp. 2257–2260).Google Scholar
  8. 8.
    Lee, J. H., & Buehrer, R. M. (2009). Location estimation using differential RSS with spatially correlated shadowing. In Global telecommunications conference (GLOBECOM 2009) (pp. 1–6).Google Scholar
  9. 9.
    Wang, S., & Inkol, R. (2011). A near-optimal least squares solution to received signal strength difference based geolocation. In IEEE international conference on acoustics, speech and signal processing (ICASSP 2011) (pp. 2600–2603).Google Scholar
  10. 10.
    Jackson, B. R., Wang, S., & Inkol, R. (2011). Received signal strength difference emitter geolocation least squares algorithm comparison. In 24th Canadian conference on electrical and computer engineering (CCECE 2011) (pp. 001113–001118).Google Scholar
  11. 11.
    Lee, J. H. (2011). Physical layer security for wireless position location in the presence of location spoofing. Ph.D. Dissertation, Virginia Technology University.Google Scholar
  12. 12.
    Lee, J. H., & Buehrer, R. M. (2012). Fundamentals of received signal strength-based position location. In R. Zekavat & R. M. Buehrer (Eds.), Handbook of position location: Theory, practice, and advances (pp. 359–394). Singapore: Wiley.Google Scholar
  13. 13.
    Lin, L., So, H. C., & Chan, Y. T. (2013). Accurate and simple source localization using differential received signal strength. Digital Signal Processing, 23(3), 736–743.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wang, S., Inkol, R., & Jackson, B. R. (2012). Relationship between the maximum likelihood emitter location estimators based on received signal strength (RSS) and received signal strength difference (RSSD). In 26th Biennial symposium on communications (QBSC 2012) (pp. 64–69).Google Scholar
  15. 15.
    Taylor, R. C. (2013). Received signal strength-based localization of non-collaborative emitters in the presence of correlated shadowing. M.S. Thesis, Virginia Technology University.Google Scholar
  16. 16.
    Tsui, A. W., Chuang, Y. H., & Chu, H. H. (2009). Unsupervised learning for solving RSS hardware variance problem in WiFi localization. Mobile Networks and Applications, 14(5), 677–691.CrossRefGoogle Scholar
  17. 17.
    Cheng, W., Tan, K., Omwando, V., Zhu, J., & Mohapatra, P. (2013). RSS-ratio for enhancing performance of RSS-based applications. In INFOCOM 2013 (pp. 3075–3083).Google Scholar
  18. 18.
    Cai, L., Zeng, K., Chen, H., & Mohapatra, P. (2011). Good neighbor: Secure pairing of nearby wireless devices by multiple antennas. In 18th Annual network and distributed system security symposium.Google Scholar
  19. 19.
    Lohrasbipeydeh, H., Gulliver, T. A., & Amindavar, H. (2014). Blind received signal strength difference based source localization with system parameter errors. IEEE Transactions on Signal Processing, 62(17), 4516–4531.MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Sternowski, R. (2016) Power difference of arrival geolocation. U.S. Patent No. 9,316,719. 19 April 2016.Google Scholar
  21. 21.
    Li, X. (2006). RSS-based location estimation with unknown pathloss model. IEEE Transactions on Wireless Communications, 5(12), 3626–3633.CrossRefGoogle Scholar
  22. 22.
    Shirahama, J., & Ohtsuki, T. (2008). RSS-based localization in environments with different path loss exponent for each link. In Vehicular technology conference (pp. 1509–1513).Google Scholar
  23. 23.
    Mao, G., Anderson, B. D., & Fidan, B. (2007). Path loss exponent estimation for wireless sensor network localization. Computer Networks, 51(10), 2467–2483.CrossRefzbMATHGoogle Scholar
  24. 24.
    Chan, Y. T., Lee, B. H., Inkol, R., & Chan, F. (2011). Received signal strength localization with an unknown path loss exponent. In 24th Canadian conference on electrical and computer engineering (CCECE) (pp. 456–459).Google Scholar
  25. 25.
    Wang, G. C., Chen, H., Li, Y., & Jin, M. (2012). On received-signal-strength based localization with unknown transmit power and path loss exponent. Wireless Communications Letters, 1(5), 536–539.CrossRefGoogle Scholar
  26. 26.
    Salman, N. G. (2012). On the joint estimation of the RSS-based location and path-loss exponent. Wireless Communications Letters, 1(1), 34–37.CrossRefGoogle Scholar
  27. 27.
    Chan, Y. T., Lee, B. H., Inkol, R. J., & Chan, F. (2012). Estimation of emitter power, location, and path loss exponent. In Proceedings of CCECE (pp. 1–5).Google Scholar
  28. 28.
    Gholami, M. R., Vaghefi, R. M., & Ström, E. G. (2013). RSS-based sensor localization in the presence of unknown channel parameters. IEEE Transactions on Signal Processing, 61(15), 3752–3759.MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Scerri, P., Glinton, R., Owens, S., Scerri, D., & Sycara, K. (2007). Geolocation of RF emitters by many UAVs. In AIAA Infotech@ Aerospace 2007 Conference and Exhibit (pp. 2858).Google Scholar
  30. 30.
    Dehghan, S. M. M., Moradi, H., & Shahidian, S. A. A. (2014). Optimal path planning for DRSSI based localization of an RF source by multiple UAVs. In RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) (pp. 558–563).Google Scholar
  31. 31.
    Shahidian, S. A. A., & Soltanizadeh, H. (2015). Optimal trajectories for two UAVs in localization of multiple RF sources. Transactions of the Institute of Measurement and Control, 38(8), 908–916.CrossRefGoogle Scholar
  32. 32.
    Shin, H. S., de Corlieu, T., & Tsourdos, A. (2014). Civil GPS jammer geolocation from a UAV equipped with a received signal strength indicator sensor. In Proceedings of international conference on intelligent unmanned systems.Google Scholar
  33. 33.
    Sono, H., Ohno, A., Kojima, T., & Yamane, Y. (2007). Retrospective estimation of the spatial dose distribution and the number of fissions in criticality accident using area dosimeters. Journal of Nuclear Science and Technology, 44(1), 43–53.CrossRefGoogle Scholar
  34. 34.
    Traa, J. (2013). Least squares intersection of lines. http://cal.cs.illinois.edu/~johannes/research/LS_line_intersect.pdf. Accessed 01 Feb 2015.
  35. 35.
    Kay, S. M. (1993). Fundamentals of statistical signal processing, Volume I: Estimation theory. Upper Saddle River, NJ: Prentice Hall.zbMATHGoogle Scholar
  36. 36.
    Patwari, N., Hero, A. O., Perkins, M., Correal, N. S., & O’dea, R. J. (2003). Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 51(8), 2137–2148.CrossRefGoogle Scholar
  37. 37.
    MATLAB 2014b, The MathWorks, Inc., Natick, Massachusetts, United States, License Number: 991708.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringAnadolu UniversityEskisehirTurkey

Personalised recommendations