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Enhancing Positioning Accuracy Through RSS Based Ranging and Weighted Least Square Approximation

Enhancing Positioning Accuracy Through RSS Based Ranging and Weighted Least Square Approximation. Mohamed Laaraiedh Stéphane Avrillon Bernard Uguen POCA 09 - Antwerpen May 28, 2009 IETR Labs http://www.ietr.org University of Rennes 1.

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Enhancing Positioning Accuracy Through RSS Based Ranging and Weighted Least Square Approximation

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  1. Enhancing Positioning Accuracy Through RSS Based Ranging and Weighted Least Square Approximation Mohamed Laaraiedh Stéphane Avrillon Bernard Uguen POCA 09 - Antwerpen May 28, 2009 IETR Labs http://www.ietr.org University of Rennes 1 How to enhance RSS based ranging and localization by learning the channel ?

  2. Context and Motivations • RSS measurements are less accurate then time based observables (ToA,TDoA) • RSS is usually available for free • RSS can be modelled as a function of distance: Path Loss models • Path loss models can be updated using RSS measurements • How can the knowledge of channel enhance RSS based localization and ranging accuracies? 1/12 1/12

  3. Learning of radio propagation channel RSS based ranging estimators RSS based Localization Simulations and Results Conclusions and Perspectives Outline 2/12

  4. Ranging and Localization … RSS1 RSS2 RSSn Ranging Step … r1 r2 rn Range Based Estimator Localization Step WLS LS Position x 3/12

  5. To get more sophisticated estimators of position, variances must be considered. Indirect estimators: RSS ranging 4/12

  6. How to improve Path Loss Model relevance ? For each fixed AP or BS Continuously update and keep track of 3 numbers Learning of Radio Channel It is necessary to learn the Path Loss Model Parameters from the channel. 5/12

  7. LS and WLS Approximations evaluated from K anchor nodes positions evaluated from estimated ranges and anchor nodes coordinates LS estimator WLS estimator 6/12

  8. Simulations and Results 7/12

  9. Simulations and Results Performances in outdoor scenario 8/12

  10. Simulations and Results Performances in indoor scenario 9/12

  11. Conclusions & Perspectives How interesting is the learning of channel for localization and ranging. A new ML estimator of Ranges from RSS observables is proposed. Localization and Ranging accuracies depend on PL parameters. Localization accuracy depends on the used technique for RSS ranging. Evaluate these estimators on Real Measurements and Ray tracing simulations. Pipe these estimators in Tracking processes using Klaman and Particle Filters. A direct approach for RSS based localization is already published in VTC Spring 10/12

  12. Bibliography [1] P. Bellavista, A. Kupper, and S. Helal, “Location-based services: Back to the future,” IEEE, Pervasive Computing, 2008. [2] “http://www.kn-s.dlr.de/where/.” [3] H. Laitinen, S. Juurakko, T. Lahti, R. Korhonen, and J. Lahteenmaki, “Experimental evaluation of location methods based on signal-strength measurements,” IEEE transactions on vehicular technology, vol. 56, Jan. 2007. [4] A. Goldsmith, Wireless communications. 2005. [5] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on systems, man, and cybernetics, vol. 37, Nov. 2007. [6] K. Cheung, H. So, W. Ma, and Y. Chan, “A constrained least squares approach to mobile positioning: Algorithms and optimality,” 2006. [7] T. Gigl, G. J. M. Janssen, V. Dizdarevic, K. Witrisal, and Z. Irahhauten, “Analysis of a uwb indoor positioning system based on received signal strength,” WPNC 07, 2007. [8] M. Sugano and T. Kawazoe, “Indoor localization system using rssi measurement of wireless sensor network based on zigbee standard,” WSN 06, July 2006. [9] S. Frattasi, M. Monti, and P. Ramjee, “A cooperative localization scheme for 4g wireless communications,” IEEE Radio and Wireless Symposium, 2006. [10] V. Abhayawardhana, W. Crosby, M. Sellars, and M. Brown, “Comparison of empirical propagation path loss models for fixed wireless access systems,” IEEE VTC spring, 2005. [11] K. Whitehouse, C. Karlof, and D. Culler, “A practical evaluation of radio signal strength for ranging-based localization,” Mobile Computing and Communications Review, vol. 11, no. 1, 2007. [12] M. P.McLaughlin, A Compendium of Common Probability Distributions, vol. Regress+ Documentation. 1999. [13] M.Laaraiedh, S.Avrillon, B.Uguen. Hybrid Data Fusion Techniques for Localization in UWB Networks. In Proceedings WPNC Hanover, Germany, March 2009.[14] S. Sand, C. Mensing, M. Laaraiedh, B. Uguen, B. Denis, S. Mayrargue, M. García, J. Casajús, D. Slock, T. Pedersen, X. Yin, G. Steinboeck, and B. H. Fleury. Performance Assessment of Hybrid Data Fusion and Tracking Algorithms. In Accepted for publication in Proceedings ICT Mobile Summit (ICT Summit 2009), Santander, Spain, June 2009.[15] M.Laaraiedh, S.Avrillon, B.Uguen. Enhancing positioning accuracy through RSS based ranging and weighted least square approximation. POCA, Antwerp, Belgium, May, 2009. 11/12

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