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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Dynamic threshold location algorithm based on fingerprinting method

  • Ding, Xuxing (College of Physics and Electronic Information, Anhui Normal University) ;
  • Wang, Bingbing (College of Physics and Electronic Information, Anhui Normal University) ;
  • Wang, Zaijian (College of Physics and Electronic Information, Anhui Normal University)
  • Received : 2017.08.31
  • Accepted : 2018.03.27
  • Published : 2018.08.07
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Abstract

The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K-Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of $20m{\times}20m$, $30m{\times}30m$, $40m{\times}40m$ and $50m{\times}50m$. Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.

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References

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