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Data-efficient Gaussian process regression for accurate visible light positioning

(2020) IEEE COMMUNICATIONS LETTERS. 24(8). p.1705-1709
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Abstract
In the field of indoor localization systems, Received Signal Strength (RSS) based Visible Light Positioning (VLP) has gained increased attention due to the dual functionality of lighting and localization. Previously geometrical models have been used to determine the position of a mobile entity, however these are unsuited when dealing with tilted surfaces and non-Lambertian sources. For this reason, machine learning techniques like Multi Layer Perceptrons (MLPs) have been considered recently. In this work, Gaussian Processes (GPs) are introduced in the context of RSS-based VLP, since they have proven to work well when using small, noisy datasets for different applications. Their performance is evaluated using both simulated data with a small transmitter tilt tolerance and measurements. It is demonstrated that the GP model outperforms both the multilateration approach and the MLP approach for the simulations and measurements data.
Keywords
Light emitting diodes, Gaussian processes, Maximum likelihood, estimation, Receivers, Optical transmitters, Training, Kernel, Visible, light positioning, gaussian process, machine learning

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MLA
Knudde, Nicolas, et al. “Data-Efficient Gaussian Process Regression for Accurate Visible Light Positioning.” IEEE COMMUNICATIONS LETTERS, vol. 24, no. 8, IEEE, 2020, pp. 1705–09, doi:10.1109/LCOMM.2020.2990950.
APA
Knudde, N., Raes, W., De Bruycker, J., Dhaene, T., & Stevens, N. (2020). Data-efficient Gaussian process regression for accurate visible light positioning. IEEE COMMUNICATIONS LETTERS, 24(8), 1705–1709. https://doi.org/10.1109/LCOMM.2020.2990950
Chicago author-date
Knudde, Nicolas, Willem Raes, Jorik De Bruycker, Tom Dhaene, and Nobby Stevens. 2020. “Data-Efficient Gaussian Process Regression for Accurate Visible Light Positioning.” IEEE COMMUNICATIONS LETTERS 24 (8): 1705–9. https://doi.org/10.1109/LCOMM.2020.2990950.
Chicago author-date (all authors)
Knudde, Nicolas, Willem Raes, Jorik De Bruycker, Tom Dhaene, and Nobby Stevens. 2020. “Data-Efficient Gaussian Process Regression for Accurate Visible Light Positioning.” IEEE COMMUNICATIONS LETTERS 24 (8): 1705–1709. doi:10.1109/LCOMM.2020.2990950.
Vancouver
1.
Knudde N, Raes W, De Bruycker J, Dhaene T, Stevens N. Data-efficient Gaussian process regression for accurate visible light positioning. IEEE COMMUNICATIONS LETTERS. 2020;24(8):1705–9.
IEEE
[1]
N. Knudde, W. Raes, J. De Bruycker, T. Dhaene, and N. Stevens, “Data-efficient Gaussian process regression for accurate visible light positioning,” IEEE COMMUNICATIONS LETTERS, vol. 24, no. 8, pp. 1705–1709, 2020.
@article{8673379,
  abstract     = {In the field of indoor localization systems, Received Signal Strength (RSS) based Visible Light Positioning (VLP) has gained increased attention due to the dual functionality of lighting and localization. Previously geometrical models have been used to determine the position of a mobile entity, however these are unsuited when dealing with tilted surfaces and non-Lambertian sources. For this reason, machine learning techniques like Multi Layer Perceptrons (MLPs) have been considered recently. In this work, Gaussian Processes (GPs) are introduced in the context of RSS-based VLP, since they have proven to work well when using small, noisy datasets for different applications. Their performance is evaluated using both simulated data with a small transmitter tilt tolerance and measurements. It is demonstrated that the GP model outperforms both the multilateration approach and the MLP approach for the simulations and measurements data.},
  author       = {Knudde, Nicolas and Raes, Willem and De Bruycker, Jorik and Dhaene, Tom and Stevens, Nobby},
  issn         = {1089-7798},
  journal      = {IEEE COMMUNICATIONS LETTERS},
  keywords     = {Light emitting diodes,Gaussian processes,Maximum likelihood,estimation,Receivers,Optical transmitters,Training,Kernel,Visible,light positioning,gaussian process,machine learning},
  language     = {eng},
  number       = {8},
  pages        = {1705--1709},
  publisher    = {IEEE},
  title        = {Data-efficient Gaussian process regression for accurate visible light positioning},
  url          = {http://dx.doi.org/10.1109/LCOMM.2020.2990950},
  volume       = {24},
  year         = {2020},
}

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