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The Sectored Antenna Array Indoor Positioning System with Neural Networks

Received: 24 March 2016     Published: 25 March 2016
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Abstract

This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.

Published in Automation, Control and Intelligent Systems (Volume 4, Issue 2)
DOI 10.11648/j.acis.20160402.13
Page(s) 21-27
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Sectored Antenna, Indoor Positioning System (IPS), Neural Network (NN), Received Signal Strength (RSS)

References
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Cite This Article
  • APA Style

    Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang. (2016). The Sectored Antenna Array Indoor Positioning System with Neural Networks. Automation, Control and Intelligent Systems, 4(2), 21-27. https://doi.org/10.11648/j.acis.20160402.13

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    ACS Style

    Chih-Yung Chen; Yu-Ju Chen; Ya-Chen Weng; Shen-Whan Chen; Rey-Chue Hwang. The Sectored Antenna Array Indoor Positioning System with Neural Networks. Autom. Control Intell. Syst. 2016, 4(2), 21-27. doi: 10.11648/j.acis.20160402.13

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    AMA Style

    Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang. The Sectored Antenna Array Indoor Positioning System with Neural Networks. Autom Control Intell Syst. 2016;4(2):21-27. doi: 10.11648/j.acis.20160402.13

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  • @article{10.11648/j.acis.20160402.13,
      author = {Chih-Yung Chen and Yu-Ju Chen and Ya-Chen Weng and Shen-Whan Chen and Rey-Chue Hwang},
      title = {The Sectored Antenna Array Indoor Positioning System with Neural Networks},
      journal = {Automation, Control and Intelligent Systems},
      volume = {4},
      number = {2},
      pages = {21-27},
      doi = {10.11648/j.acis.20160402.13},
      url = {https://doi.org/10.11648/j.acis.20160402.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.13},
      abstract = {This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - The Sectored Antenna Array Indoor Positioning System with Neural Networks
    AU  - Chih-Yung Chen
    AU  - Yu-Ju Chen
    AU  - Ya-Chen Weng
    AU  - Shen-Whan Chen
    AU  - Rey-Chue Hwang
    Y1  - 2016/03/25
    PY  - 2016
    N1  - https://doi.org/10.11648/j.acis.20160402.13
    DO  - 10.11648/j.acis.20160402.13
    T2  - Automation, Control and Intelligent Systems
    JF  - Automation, Control and Intelligent Systems
    JO  - Automation, Control and Intelligent Systems
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    EP  - 27
    PB  - Science Publishing Group
    SN  - 2328-5591
    UR  - https://doi.org/10.11648/j.acis.20160402.13
    AB  - This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer and Communication, Shu-Te University, Kaohsiung City, Taiwan

  • Department of Information Management, Cheng Shiu University, Kaohsiung City, Taiwan

  • Department of Computer and Communication, Shu-Te University, Kaohsiung City, Taiwan

  • Department of Communication Engineering, I-Shou University, Kaohsiung City, Taiwan

  • Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan

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