Adaptive Localization in Wireless Networks

  • Henning Lenz
  • Bruno Betoni Parodi
  • Hui Wang
  • Andrei Szabo
  • Joachim Bamberger
  • Dragan Obradovic
  • Joachim Horn
  • Uwe D. Hanebeck

Indoor positioning approaches based on communication systems typically use the received signal strength (RSS) as measurements. To work properly, such a system often requires many calibration points before its start. Based on theoretical-propagation models (RF planning) and on self-organizing maps (SOM) an adaptive approach for Simultaneous Localization and Learning (SLL) has been developed. The algorithm extracts out of online measurements the true model of the RF propagation. Applying SLL, a self-calibrating RSS-based positioning system with high accuracies can be realized without the need of cost intensive calibration measurements during system installation or maintenance. The main aspects of SLL are addressed as well as convergence and statistical properties. Results for real-world DECT and WLAN setups are given, showing that the localization starts with a basic performance slightly better than Cell-ID, finally reaching the accuracy of pattern matching using calibration points.


Wireless Network Receive Signal Strength Near Neighbor Calibration Point Real Localization 
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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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Henning Lenz
    • 1
  • Bruno Betoni Parodi
    • 1
  • Hui Wang
    • 1
  • Andrei Szabo
    • 1
  • Joachim Bamberger
    • 1
  • Dragan Obradovic
    • 1
  • Joachim Horn
    • 2
  • Uwe D. Hanebeck
    • 3
  1. 1.Siemens AGGermany
  2. 2.University of the Federal Armed ForcesGermany
  3. 3.Universitt KarlsruheGermany

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