Abstract

In this work, a novel iterative algorithm based on an M5 model tree (M5Tree) is proposed to realize indoor positioning using a single LED and multiple photodetectors. A visible light positioning system was implemented to evaluate the algorithm performance, including the effect of the maximum depth of the tree and the number of iterations on positioning accuracy. Finally, in the same single LED positioning system, the traditional machine-learning algorithms, being k-nearest neighbor and support vector regression, were compared with the M5Tree algorithm. The simulation results demonstrate that the proposed algorithm has good positioning performance on a single LED positioning system.

© 2020 Optical Society of America

Full Article  |  PDF Article
More Like This
Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning

Zhuo Zhang, Yaguang Zhu, Wentao Zhu, Huayang Chen, Xuezhi Hong, and Jiajia Chen
Opt. Express 27(16) 22161-22172 (2019)

Hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning

Heqing Huang, Bo Lin, Lihui Feng, and Huichao Lv
Appl. Opt. 58(12) 3214-3221 (2019)

Received signal strength assisted perspective-three-point algorithm for indoor visible light positioning

Lin Bai, Yang Yang, Chunyan Feng, and Caili Guo
Opt. Express 28(19) 28045-28059 (2020)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (6)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (2)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (13)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription