Advanced search
1 file | 1.12 MB Add to list

A positioning algorithm for VLP in the presence of orientation uncertainty

(2019) SIGNAL PROCESSING. 160. p.13-20
Author
Organization
Abstract
As the positioning accuracy of a visible light positioning (VLP) system is highly susceptible to changes in the orientation of the receiver, accurate knowledge of the receiver orientation is required. In practice, the orientation of the receiver is estimated with an external orientation estimation device. However, these devices generally suffer from drift and misalignment, causing an uncertainty in the measured orientation that will degrade the performance of standard positioning algorithms. In this paper, we derive a novel positioning algorithm that takes into account the effect of the orientation uncertainty. To this end, we need to cope with the non-linear relationship between the received signal strength (RSS) and the orientation uncertainty, which makes the likelihood function of the RSS, required to derive the maximum likelihood (ML) estimator, hard to obtain. To solve this issue, we consider the first and second-order Taylor series expansion of the RSS. Although the accuracy of the second-order approximation is better than the first-order approximation, the first-order approximation results in a closed-form expression for the likelihood function, while this is not possible with the second-order approximation. Because of this, we derive the ML estimator using the first-order approximation, and employ the multivariate gradient descent algorithm to obtain the position estimate. Computer simulations show that the proposed algorithm outperforms state-of-the-art VLP algorithms subject to orientation uncertainty. (C) 2019 Elsevier B.V. All rights reserved.
Keywords
VISIBLE-LIGHT, LOCALIZATION, AOA, Visible light positioning, Orientation uncertainty, Lie algebra, Approximation

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.12 MB

Citation

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

MLA
Li, Shiyin, Shengqiang Shen, and Heidi Steendam. “A Positioning Algorithm for VLP in the Presence of Orientation Uncertainty.” SIGNAL PROCESSING 160 (2019): 13–20. Print.
APA
Li, Shiyin, Shen, S., & Steendam, H. (2019). A positioning algorithm for VLP in the presence of orientation uncertainty. SIGNAL PROCESSING, 160, 13–20.
Chicago author-date
Li, Shiyin, Shengqiang Shen, and Heidi Steendam. 2019. “A Positioning Algorithm for VLP in the Presence of Orientation Uncertainty.” Signal Processing 160: 13–20.
Chicago author-date (all authors)
Li, Shiyin, Shengqiang Shen, and Heidi Steendam. 2019. “A Positioning Algorithm for VLP in the Presence of Orientation Uncertainty.” Signal Processing 160: 13–20.
Vancouver
1.
Li S, Shen S, Steendam H. A positioning algorithm for VLP in the presence of orientation uncertainty. SIGNAL PROCESSING. Amsterdam: Elsevier ; 2019;160:13–20.
IEEE
[1]
S. Li, S. Shen, and H. Steendam, “A positioning algorithm for VLP in the presence of orientation uncertainty,” SIGNAL PROCESSING, vol. 160, pp. 13–20, 2019.
@article{8620116,
  abstract     = {As the positioning accuracy of a visible light positioning (VLP) system is highly susceptible to changes in the orientation of the receiver, accurate knowledge of the receiver orientation is required. In practice, the orientation of the receiver is estimated with an external orientation estimation device. However, these devices generally suffer from drift and misalignment, causing an uncertainty in the measured orientation that will degrade the performance of standard positioning algorithms. In this paper, we derive a novel positioning algorithm that takes into account the effect of the orientation uncertainty. To this end, we need to cope with the non-linear relationship between the received signal strength (RSS) and the orientation uncertainty, which makes the likelihood function of the RSS, required to derive the maximum likelihood (ML) estimator, hard to obtain. To solve this issue, we consider the first and second-order Taylor series expansion of the RSS. Although the accuracy of the second-order approximation is better than the first-order approximation, the first-order approximation results in a closed-form expression for the likelihood function, while this is not possible with the second-order approximation. Because of this, we derive the ML estimator using the first-order approximation, and employ the multivariate gradient descent algorithm to obtain the position estimate. Computer simulations show that the proposed algorithm outperforms state-of-the-art VLP algorithms subject to orientation uncertainty. (C) 2019 Elsevier B.V. All rights reserved.},
  author       = {Li, Shiyin and Shen, Shengqiang and Steendam, Heidi},
  issn         = {0165-1684},
  journal      = {SIGNAL PROCESSING},
  keywords     = {VISIBLE-LIGHT,LOCALIZATION,AOA,Visible light positioning,Orientation uncertainty,Lie algebra,Approximation},
  language     = {eng},
  pages        = {13--20},
  publisher    = {Elsevier },
  title        = {A positioning algorithm for VLP in the presence of orientation uncertainty},
  url          = {http://dx.doi.org/10.1016/j.sigpro.2019.02.014},
  volume       = {160},
  year         = {2019},
}

Altmetric
View in Altmetric
Web of Science
Times cited: