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Self-localization Based on Ambient Signals

  • Thomas Janson
  • Christian Schindelhauer
  • Johannes Wendeberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6451)

Abstract

We present an approach for the localization of passive nodes in a communication network using ambient radio or sound signals. In our settings the communication nodes have unknown positions. They are synchronized but do not emit signals for localization and exchange only the time points when environmental signals are received, the time differences of arrival (TDOA). The signals occur at unknown positions and times, but can be distinguished. Since no anchors are available, the goal is to determine the relative positions of all communication nodes and the environmental signals.

The Ellipsoid TDOA method introduces a closed form solution assuming the signals originate from far distances. The TDOA characterize an ellipse from which the distances and angles between three network nodes can be inferred.

The approach is tested in numerous simulations and in indoor and outdoor settings where the relative positions of mobile devices are determined utilizing only the sound produced by assistants with noisemakers.

Keywords

Wireless Sensor Network Sound Source Wireless Local Area Network Round Trip Time Receive Signal Strength Indication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Drane, C., Macnaughtan, M., Scott, C.: Positioning GSM Telephones. IEEE Communications Magazine 36, 46–54 (1998)CrossRefGoogle Scholar
  2. 2.
    Otsason, V., Varshavsky, A., LaMarca, A., de Lara, E.: Accurate GSM Indoor Localization. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 141–158. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Sichitiu, M.L., Ramadurai, V.: Localization of Wireless Sensor Networks with a Mobile Beacon. In: Proceedings of the First IEEE Conference on Mobile Ad-hoc and Sensor Systems, pp. 174–183 (2004)Google Scholar
  4. 4.
    Biswas, P., Ye, Y.: Semidefinite Programming for Ad Hoc Wireless Sensor Network Localization. In: IPSN 2004: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, pp. 46–54. ACM, New York (2004)Google Scholar
  5. 5.
    El Ghaoui, L., Doherty, L., Pister, K.S.J.: Convex position estimation in wireless sensor networks. In: Proceedings of Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2001, vol. 3, pp. 1655–1663. IEEE, Los Alamitos (2001)Google Scholar
  6. 6.
    Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The Cricket Location-Support System. In: MobiCom 2000: Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 32–43 (2000)Google Scholar
  7. 7.
    Wang, Z., Zekavat, S.(R.).A.: A Novel Semidistributed Localization Via Multinode TOA-DOA Fusion. IEEE Transactions on Vehicular Technology 58(7), 3426–3435 (2009)Google Scholar
  8. 8.
    Ferris, B., Hähnel, D., Fox, D.: Gaussian Processes for Signal Strength-Based Location Estimation. In: Proceedings of Robotics: Science and Systems Conference, RSS (2006)Google Scholar
  9. 9.
    Yang, L., Ho, K.C.: An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions. IEEE Transactions on Signal Processing 57, 4598–4615 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gillette, M.D., Silverman, H.F.: A Linear Closed-Form Algorithm for Source Localization From Time-Differences of Arrival. IEEE Signal Processing Letters 15, 1–4 (2008)CrossRefGoogle Scholar
  11. 11.
    Carevic, D.: Automatic Estimation of Multiple Target Positions and Velocities Using Passive TDOA Measurements of Transients. IEEE Transactions on Signal Processing 55, 424–436 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Rui, Y., Florencio, D.: New direct approaches to robust sound source localization. In: Proc. of IEEE ICME 2003, pp. 6–9. IEEE, Los Alamitos (2003)Google Scholar
  13. 13.
    Valin, J.-M., Michaud, F., Rouat, J., Létourneau, D.: Robust Sound Source Localization Using a Microphone Array on a Mobile Robot. In: Proceedings International Conference on Intelligent Robots and Systems (IROS), pp. 1228–1233 (2003)Google Scholar
  14. 14.
    Moses, R.L., Krishnamurthy, D., Patterson, R.M.: A Self-Localization Method for Wireless Sensor Networks. EURASIP Journal on Advances in Signal Processing, 348–358 (2003)Google Scholar
  15. 15.
    Raykar, V.C., Kozintsev, I., Lienhart, R.: Position calibration of audio sensors and actuators in a distributed computing platform. In: Proceedings of the Eleventh ACM International Conference on Multimedia, p. 581. ACM, New York (2003)Google Scholar
  16. 16.
    Lim, H., Kung, L.-C., Hou, J.C., Luo, H.: Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure. Wirel. Netw. 16(2), 405–420 (2010)CrossRefGoogle Scholar
  17. 17.
    Gander, W., Golub, G.H., Strebel, R.: Least-Square Fitting of Circles and Ellipses. BIT Numerical Mathematics 34(4), 558–578 (1994)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Thomas Janson
    • 1
  • Christian Schindelhauer
    • 1
  • Johannes Wendeberg
    • 1
  1. 1.Computer Networks and TelematicsUniversity of FreiburgGermany

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