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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)

Abstract

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.

Keywords

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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