Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (2): 34-39.doi: 10.16180/j.cnki.issn1007-7820.2022.02.006

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Fingerprint Location Method of Metro Station Based on GAWK-means

JIN Xiao,WU Fei,YAN Song,LU Wenxia,ZHANG Zhongyi   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science, Shanghai 201620,China
  • Received:2020-10-30 Online:2022-02-15 Published:2022-02-24
  • Supported by:
    National Natural Science Foundation of China(61902237);Key Projects of Shanghai Science and Technology Academic Committee(18511101600);Shanghai Natural Science Foundation(17ZR1411900);Shanghai Science and Technology Commission Young Scientific and Technological Talents "Yang Fan Plan"(19YF1418200);Graduate Scientific Research and Innovation Program of Shanghai University of Engineering Science(19KY0207)


In order to solve the problem of low matching efficiency and poor positioning accuracy when using iBeacon technology for fingerprint location in urban rail transit stations, a metro station fingerprint location method based on GAWK-means is proposed in this study. In the offline stage, the K-means Euclidean distance weight is optimized according to the discreteness of the fingerprint data to better reflect the intra-class similarity. Then, the improved K-means is combined with the genetic algorithm to optimize the clustering results to reduce the clustering results from falling into the local optimum. In the online stage, the K-nearest neighbor method is used to match the signal vector with the nearest sub-fingerprint database to get the location result, and the overall performance of the method is evaluated by the average positioning error. The experimental results show that the average positioning error of the GAWK-means algorithm is 1.52 m in the offline phase of the subway station. Compared with the un-clustered and traditional K-means clustering, the positioning error of the proposed method is reduced by more than 0.41 m.

Key words: metro station, iBeacon technology, fingerprint location, genetic algorithm, K-means clustering, Euclidean distance, K-nearest neighbor method, GAWK-means

CLC Number: 

  • TN92