An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks

  • Johannes Schmid
  • Frederik Beutler
  • Benjamin Noack
  • Uwe D. Hanebeck
  • Klaus D. Müller-Glaser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6567)


In this paper, the localization of persons by means of a Wireless Sensor Network (WSN) is considered. Persons carry on-body sensor nodes and move within a WSN. The location of each person is calculated on this node and communicated through the network to a central data sink for visualization. Applications of such a system could be found in mass casualty events, firefighter scenarios, hospitals or retirement homes for example.

For the location estimation on the sensor node, three derivatives of the Kalman filter and a closed-form solution (CFS) are applied, compared, and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN is implemented and data are collected in in- and outdoor environments with differently positioned on-body nodes. The described estimators are then evaluated off-line on the experimentally collected data.

The goal of this paper is to present a comprehensive real-world evaluation of methods for person localization in a WSN based on received signal strength (RSS) range measurements. It is concluded that person localization in in- and outdoor environments is possible under the considered conditions with the considered filters. The compared methods allow for sufficiently accurate localization results and are robust against inaccurate range measurements.


Root Mean Square Error Sensor Node Wireless Sensor Network Receive Signal Strength Extend Kalman Filter 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Johannes Schmid
    • 1
  • Frederik Beutler
    • 2
  • Benjamin Noack
    • 2
  • Uwe D. Hanebeck
    • 2
  • Klaus D. Müller-Glaser
    • 1
  1. 1.Institute for Information Processing Technology (ITIV)Germany
  2. 2.Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for AnthropomaticsKarlsruhe Institute of Technology (KIT)Germany

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