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Parameters Optimization for Support Vector Regression Based Indoor Visible Light Localization

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Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11643))

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Abstract

In this paper, we optimize parameters of indoor visible light localization system based on support vector regression algorithm to achieve higher positioning accuracy. Additionally, some other popular supervised machine learning algorithms such as linear regression, artificial neural networks, and k-nearest neighbors are also implemented. Then, we compare these solutions together to demonstrate the superiority of our approach. This solution is simulated in a hypothetical space of 5 m × 5 m × 3 m. To obtain satisfactory performance, a system of four LED lights and a photodiode are used to transmit and receive optical power, respectively. In the proposed system, the location estimation process is divided into two distinct sub-processes: offline stage and online stage. In the offline stage, data collection and data training are carried out. The results obtained from this stage and the current data in the online stage are the base to determine the current location of the object carrying the photodiode. The simulation results prove that our approach can achieve positioning accuracy of almost 7.4 cm.

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Acknowledgments

This work was supported by Korea Hydro & Nuclear Power company through the project “Nuclear Innovation Center for Haeoleum Alliance”.

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Authors and Affiliations

  1. Robotics and Mechatronics Lab, Ulsan University, Ulsan, 44610, Republic of Korea

    Huy Q. Tran & Cheolkeun Ha

Authors
  1. Huy Q. Tran
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  2. Cheolkeun Ha
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Corresponding author

Correspondence to Cheolkeun Ha .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Tran, H.Q., Ha, C. (2019). Parameters Optimization for Support Vector Regression Based Indoor Visible Light Localization. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_58

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  • DOI: https://doi.org/10.1007/978-3-030-26763-6_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26762-9

  • Online ISBN: 978-3-030-26763-6

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