Wi-Fi RSS Based Indoor Positioning Using a Probabilistic Reduced Estimator

  • Gang Shen
  • Zegang Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)


In this paper, we present an investigation of indoor objects positioning using the received Wi-Fi signal strength in the realistic environment with the presence of obstacles. Wi-Fi RSS based positioning is a promising alternative to other techniques for locating indoor objects. Two factors may lead to the low Wi-Fi RSS positioning accuracy: the existence of moving obstacles, and the limited number of available anchor nodes. We propose a novel approach to locating a target object in a given area by introducing a hidden factor for a reduced form of probabilistic estimator. This estimator is unbiased with the scalability in field size. With the selection of a Gaussian prior on this hidden factor characterizing the effects of RSS drop introduced by obstacles, we convert the positioning prediction into a maximum a posteriori problem, then apply expectation-maximization algorithm and conjugate gradient optimization to find the solution. Simulations in various settings show that the proposed approach presents better performance compared to other state-of-the-art RSS range-based positioning algorithms.


Indoor positioning Received Wi-Fi signal strength Maximum a posteriori Conjugate gradients 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Gang Shen
    • 1
  • Zegang Xie
    • 1
  1. 1.School of Software EngineeringHuazhong University of Science and TechnologyWuhanChina

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