• DocumentCode
    577559
  • Title

    A new indoor location technology using back propagation neural network to fit the RSSI-d curve

  • Author

    Zhang, Huiqing ; Shi, Xiaowei

  • Author_Institution
    Coll. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
  • fYear
    2012
  • fDate
    6-8 July 2012
  • Firstpage
    80
  • Lastpage
    83
  • Abstract
    After lots of research and analysis of the RSSI-d(received signal strength indicator RSSI and distanced) relationship between the reference nodes and blind nodes, A new indoor WLAN location technology using BP neural network to fit the RSSI-d curve is proposed. Firstly, establish a three-layer BP neural network, the input layer of the network is RSSI, through the hidden layer processing, the final output from the output layer is distance d between the reference node and blind node. Once get three more such distance d, according to the known coordinates of the reference nodes, Taylor series expansion algorithm is used to determine the coordinates of the blind node. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.
  • Keywords
    backpropagation; indoor communication; neural nets; series (mathematics); signal processing; wireless LAN; RSSI-d curve; Taylor series expansion algorithm; back propagation neural network; blind nodes; hidden layer processing; indoor WLAN location technology; indoor location technology; positioning accuracy; positioning algorithms; received signal strength indicator RSSI and distanced; reference nodes; three-layer BP neural network; Accuracy; Algorithm design and analysis; Biological neural networks; Educational institutions; Taylor series; Wireless communication; Back propagation neural network; Indoor location; RSSI; Taylor Series; WLAN;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2012 10th World Congress on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-1397-1
  • Type

    conf

  • DOI
    10.1109/WCICA.2012.6357843
  • Filename
    6357843