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Passive Localization Methods Exploiting Models of Distributed Natural Phenomena

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Multisensor Fusion and Integration for Intelligent Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 35))

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Abstract

This paper is devoted to methods for localizing individual sensor nodes connected in a network. The novelty of the proposed method is the model-based approach (i.e., rigorous exploitation of physical background knowledge) using local observations of a distributed phenomenon. By exploiting background phenomena, the individual sensor nodes can be localized by only locally measuring their surrounding without the necessity of heavy infrastructure. Two approaches are introduced: (a) the polynomial system localization method and (b) the simultaneous reconstruction and localization method. The first approach (PSL-method) is based on restating the mathematical model of the distributed phenomenon in terms of a polynomial system. Solving the system of polynomials for each individual sensor node directly leads to the desired locations. The second approach (SRL-method) regards the localization problem as a simultaneous state and parameter estimation problem within a Bayesian framework. By this means, the distributed phenomenon is reconstructed and the individual nodes are localized in a simultaneous fashion, while considering remaining stochastic uncertainties.

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References

  1. Felix Sawo, Thomas C. Henderson, Christopher Sikorski, , and Uwe D. Hanebeck. Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena. In Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), Seoul, Republic of Korea, August 2008.

    Google Scholar 

  2. Vesa Klumpp, Felix Sawo, Uwe D. Hanebeck, and Dietrich FrFränkennken. The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation. In Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, July 2008.

    Google Scholar 

  3. Felix Sawo, Vesa Klumpp, and Uwe D. Hanebeck. Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter. In Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, July 2008.

    Google Scholar 

  4. Felix Sawo, Kathrin Roberts, and Uwe D. Hanebeck. Bayesian Estimation of Distributed Phenomena using Discretized Representations of Partial Differential Equations. In Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006), pages 16–23, Setúbal, Portugal, August 2006.

    Google Scholar 

  5. Hui Wang, Henning Lenz, Andrei Szabo, and Uwe D. Hanebeck. Fusion of Barometric Sensors, WLAN Signals and Building Information for 3–D Indoor/Campus Localization. In Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), Heidelberg, Germany, September 2006.

    Google Scholar 

  6. David Culler, Deborah Estrin, and Mani Srivastava. Overview of Sensor Networks. IEEE Computer, 37(8):41–49, 2004.

    Google Scholar 

  7. Tong Zhao and Arye Nehorai. Detecting and Estimating Biochemical Dispersion of a Moving Source in a Semi-Infinite Medium. IEEE Transactions on Signal Processing, 54(6):2213–2225, June 2006.

    Article  Google Scholar 

  8. Thomas Bader, Alexander Wiedemann, Kathrin Roberts, and Uwe D. Hanebeck. Model–based Motion Estimation of Elastic Surfaces for Minimally Invasive Cardiac Surgery. In Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA 2007), pages 2261–2266, Rome, Italy, April 2007.

    Google Scholar 

  9. Aleksandar Jeremic and Arye Nehorai. Design of Chemical Sensor Arrays for Monitoring Disposal Sites on the Ocean Floor. IEEE Journal of Oceanic Engineering, 23:334–343, 1998.

    Article  Google Scholar 

  10. Lorenzo A. Rossi, Bhaskar Krishnamachari, and C.-C.Jay. Kuo. Distributed Parameter Estimation for Monitoring Diffusion Phenomena Using Physical Models. In First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON 2006), pages 460–469, Los Angeles, USA, 2004.

    Google Scholar 

  11. Thomas C. Henderson, Christopher Sikorski, Edwart Grant, and Kyle Luthy. Computational Sensor Networks. In Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), San Diego, USA, 2007.

    Google Scholar 

  12. Jeffrey Hightower and Gaetano Borriello. Location Systems for Ubiquitous Computing. IEEE Computer, 34(8):57–66, August 2001.

    Google Scholar 

  13. Andreas Rauh, Kai Briechle, Uwe D. Hanebeck, Joachim Bamberger, Clemens Hoffmann, and Marian Grigoras. Localization of DECT Mobile Phones Based on a New Nonlinear Filtering Technique. In Proceedings of SPIE, Vol. 5084, AeroSense Symposium, pages 39–50, Orlando, Florida, May 2003.

    Google Scholar 

  14. Ping Tao, Algis Rudys, Andrew M. Ladd, and Dan S. Wallach. Wireless LAN Location-sensing for Security Applications. In Proceedings of the 2nd ACM Workshop on Wireless Security, pages 11–20, 2003.

    Google Scholar 

  15. Andrew M. Ladd, Kostas E. Bekris, Algis Rudys, Guillaume Marceau, and Lydia E. Kavraki. Robotics-Based Location Sensing using Wireless Ethernet. In Proceedings of the 8th Annual International Conference on Mobile Computing and Networking, pages 227–238. ACM Press, 2002.

    Google Scholar 

  16. Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, and Uwe D. Hanebeck. Enhancing the Map Usage for Indoor Location-Aware Systems. In International Conference on Human-Computer Interaction (HCI 2007), Peking, China, July 2007.

    Google Scholar 

  17. Patrick Röler, Uwe D. Hanebeck, Marian Grigoras, Paul T. Pilgram, Joachim Bamberger, and Clemens Hoffmann. Automatische Kartographierung der Signalcharakteristik in Funknetz-werken. October 2003.

    Google Scholar 

  18. Temu Ross, Petri Myllymaki, Henry Tirri, Pauli Misikangas, and Juha Sievanen. A Probabilistic Approach to WLAN User Location Estimation. International Journal of Wireless Information Networks, 9(3), July 2002.

    Google Scholar 

  19. Marian Grigoras, Olga Feiermann, and Uwe D. Hanebeck. Data-Driven Modeling of Signal Strength Distributions for Localization in Cellular Radio Networks (Datengetriebene Modellierung von Feldstärkeverteilungen für die Ortung in zellulären Funknetzen). at – Automatisierungstechnik – Automatisierungstechnik, Sonderheft: Datenfusion in der Automatisierungstechnik, 53(7):314–321, July 2005.

    Google Scholar 

  20. Bruno Betoni Parodi, Andrei Szabo, Joachim Bamberger, and Joachim Horn. SPLL: Simultaneous Probabilistic Localization and Learning. In Proceedings of the 17th IFAC World Congress (IFAC 2008), Seoul, Korea, 2008.

    Google Scholar 

  21. George E. Karniadakis and Spencer J. Sherwin. Spectral/hp Element Methods for Computational Fluid Dynamics. Oxford University Press, 2005.

    Google Scholar 

  22. Thomas Schön, Fredrik Gustafsson, and Per-Johan Nordlund. Marginalized Particle Filters for Nonlinear State-space Models. Technical Report, Linköping University, 2003.

    Google Scholar 

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Correspondence to Felix Sawo .

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© 2009 Springer-Verlag Berlin Heidelberg

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Sawo, F., Henderson, T.C., Sikorski, C., Hanebeck, U.D. (2009). Passive Localization Methods Exploiting Models of Distributed Natural Phenomena. In: Hahn, H., Ko, H., Lee, S. (eds) Multisensor Fusion and Integration for Intelligent Systems. Lecture Notes in Electrical Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89859-7_26

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  • DOI: https://doi.org/10.1007/978-3-540-89859-7_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89858-0

  • Online ISBN: 978-3-540-89859-7

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