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Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning

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Abstract
In this work, the Monte Carlo algorithm to determine the variance on the distance estimation in Received Signal Strength-based visible light positioning is considered. The method is build on the maximization of the signal-to-noise-ratio by means of matched filtering, and leads to a number of characteristics that are typically only obtained after intensive analytical elaborations. It is shown that the results match those obtained by calculating the Cramer-Rao lower bound when only the noise is considered as non-deterministic. It is demonstrated that the method is also applicable when multiple physical parameters exhibit a probability distribution, leading to an assessment of the distance estimation accuracy in more realistic settings.
Keywords
Adaptive optics, Estimation, Light emitting diodes, Monte Carlo methods, Optical receivers, Photodiodes, Wireless communication

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Citation

Please use this url to cite or link to this publication:

MLA
Stevens, Nobby, et al. “Monte Carlo Algorithm for the Evaluation of the Distance Estimation Variance in RSS-Based Visible Light Positioning.” 2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), 2017, pp. 212–16.
APA
Stevens, N., Plets, D., & De Strycker, L. (2017). Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning. In 2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC) (pp. 212–216). Yogyakarta, Indonesia.
Chicago author-date
Stevens, Nobby, David Plets, and Lieven De Strycker. 2017. “Monte Carlo Algorithm for the Evaluation of the Distance Estimation Variance in RSS-Based Visible Light Positioning.” In 2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), 212–16.
Chicago author-date (all authors)
Stevens, Nobby, David Plets, and Lieven De Strycker. 2017. “Monte Carlo Algorithm for the Evaluation of the Distance Estimation Variance in RSS-Based Visible Light Positioning.” In 2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), 212–216.
Vancouver
1.
Stevens N, Plets D, De Strycker L. Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning. In: 2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC). 2017. p. 212–6.
IEEE
[1]
N. Stevens, D. Plets, and L. De Strycker, “Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning,” in 2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC), Yogyakarta, Indonesia, 2017, pp. 212–216.
@inproceedings{8557709,
  abstract     = {In this work, the Monte Carlo algorithm to determine the variance on the distance estimation in Received Signal Strength-based visible light positioning is considered. The method is build on the maximization of the signal-to-noise-ratio by means of matched filtering, and leads to a number of characteristics that are typically only obtained after intensive analytical elaborations. It is shown that the results match those obtained by calculating the Cramer-Rao lower bound when only the noise is considered as non-deterministic. It is demonstrated that the method is also applicable when multiple physical parameters exhibit a probability distribution, leading to an assessment of the distance estimation accuracy in more realistic settings.},
  author       = {Stevens, Nobby and Plets, David and De Strycker, Lieven},
  booktitle    = {2017 20TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC)},
  isbn         = {978-1-5386-2768-6},
  issn         = {1347-6890},
  keywords     = {Adaptive optics,Estimation,Light emitting diodes,Monte Carlo methods,Optical receivers,Photodiodes,Wireless communication},
  language     = {eng},
  location     = {Yogyakarta, Indonesia},
  pages        = {212--216},
  title        = {Monte Carlo algorithm for the evaluation of the distance estimation variance in RSS-based visible light positioning},
  url          = {http://dx.doi.org/10.1109/WPMC.2017.8301810},
  year         = {2017},
}

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