Abstract

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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  2. Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
    [Crossref]
  3. S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
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  4. Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
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  5. H. Hosseinianfar, M. Noshad, and M. Brandt-Pearce, “Positioning for visible light communication system exploiting multipath reflections,” in Proceedings of 2017 IEEE International Conference on Communications (ICC), Paris, 1–6 (2017).
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    [Crossref]
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    [Crossref]
  8. F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
    [Crossref]
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  10. Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
    [Crossref]
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  13. M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
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2019 (1)

F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
[Crossref]

2018 (2)

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

X. Guo, N. Ansari, L. Li, and H. Li, “Indoor localization by fusing a group of fingerprints based on random forests,” IEEE Internet of Things Journal 5(6), 4686–4698 (2018).
[Crossref]

2017 (3)

X. Guo, S. Shao, N. Ansari, and A. Khreishah, “Indoor localization using visible light via fusion of multiple classifiers,” IEEE Photonics J. 9(6), 1–16 (2017).
[Crossref]

J. Luo, L. Fan, and H. Li, “Indoor positioning systems based on visible light communication: state of the art,” IEEE Comm. Surv. and Tutor. 19(4), 2871–2893 (2017).
[Crossref]

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

2016 (3)

M. Yasir, S. Ho, and B. N. Vellambi, “Indoor position tracking using multiple optical receivers,” J. Lightwave Technol. 34(4), 1166–1176 (2016).
[Crossref]

Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
[Crossref]

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

2014 (3)

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

W. Zhang, M. I. S. Chowdhury, and M. Kavehrad, “Asynchronous indoor positioning system based on visible light communications,” Opt. Eng. 53(4), 045105 (2014).
[Crossref]

P. Wawrzyński, “Reinforcement learning with experience replay for model-free humanoid walking optimization,” International Journal of Humanoid Robotics 11(3), 137 (2014).
[Crossref]

2012 (1)

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

Ansari, N.

X. Guo, N. Ansari, L. Li, and H. Li, “Indoor localization by fusing a group of fingerprints based on random forests,” IEEE Internet of Things Journal 5(6), 4686–4698 (2018).
[Crossref]

X. Guo, S. Shao, N. Ansari, and A. Khreishah, “Indoor localization using visible light via fusion of multiple classifiers,” IEEE Photonics J. 9(6), 1–16 (2017).
[Crossref]

Brandt-Pearce, M.

H. Hosseinianfar, M. Noshad, and M. Brandt-Pearce, “Positioning for visible light communication system exploiting multipath reflections,” in Proceedings of 2017 IEEE International Conference on Communications (ICC), Paris, 1–6 (2017).
[Crossref]

Burchardt, H.

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

Burton, A.

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

Cao, P.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Cao, Y.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Charpentier, P.

F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
[Crossref]

Chen, Y.

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

Chowdhury, M. I. S.

W. Zhang, M. I. S. Chowdhury, and M. Kavehrad, “Asynchronous indoor positioning system based on visible light communications,” Opt. Eng. 53(4), 045105 (2014).
[Crossref]

El-Sheimy, N.

Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
[Crossref]

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

Fan, L.

J. Luo, L. Fan, and H. Li, “Indoor positioning systems based on visible light communication: state of the art,” IEEE Comm. Surv. and Tutor. 19(4), 2871–2893 (2017).
[Crossref]

Fang, S.

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

Guo, X.

X. Guo, N. Ansari, L. Li, and H. Li, “Indoor localization by fusing a group of fingerprints based on random forests,” IEEE Internet of Things Journal 5(6), 4686–4698 (2018).
[Crossref]

X. Guo, S. Shao, N. Ansari, and A. Khreishah, “Indoor localization using visible light via fusion of multiple classifiers,” IEEE Photonics J. 9(6), 1–16 (2017).
[Crossref]

Haas, H.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

Ho, S.

Hosseinianfar, H.

H. Hosseinianfar, M. Noshad, and M. Brandt-Pearce, “Positioning for visible light communication system exploiting multipath reflections,” in Proceedings of 2017 IEEE International Conference on Communications (ICC), Paris, 1–6 (2017).
[Crossref]

Hua, L.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Huang, T.

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

Kavehrad, M.

W. Zhang, M. I. S. Chowdhury, and M. Kavehrad, “Asynchronous indoor positioning system based on visible light communications,” Opt. Eng. 53(4), 045105 (2014).
[Crossref]

Khreishah, A.

X. Guo, S. Shao, N. Ansari, and A. Khreishah, “Indoor localization using visible light via fusion of multiple classifiers,” IEEE Photonics J. 9(6), 1–16 (2017).
[Crossref]

Krommenacker, N.

F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
[Crossref]

Le-Minh, H.

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

Li, H.

X. Guo, N. Ansari, L. Li, and H. Li, “Indoor localization by fusing a group of fingerprints based on random forests,” IEEE Internet of Things Journal 5(6), 4686–4698 (2018).
[Crossref]

J. Luo, L. Fan, and H. Li, “Indoor positioning systems based on visible light communication: state of the art,” IEEE Comm. Surv. and Tutor. 19(4), 2871–2893 (2017).
[Crossref]

Li, L.

X. Guo, N. Ansari, L. Li, and H. Li, “Indoor localization by fusing a group of fingerprints based on random forests,” IEEE Internet of Things Journal 5(6), 4686–4698 (2018).
[Crossref]

Li, Y.

Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
[Crossref]

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

Luo, J.

J. Luo, L. Fan, and H. Li, “Indoor positioning systems based on visible light communication: state of the art,” IEEE Comm. Surv. and Tutor. 19(4), 2871–2893 (2017).
[Crossref]

Noshad, M.

H. Hosseinianfar, M. Noshad, and M. Brandt-Pearce, “Positioning for visible light communication system exploiting multipath reflections,” in Proceedings of 2017 IEEE International Conference on Communications (ICC), Paris, 1–6 (2017).
[Crossref]

Qi, L.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

Seguel, F.

F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
[Crossref]

Serafimovski, N.

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

Shao, S.

X. Guo, S. Shao, N. Ansari, and A. Khreishah, “Indoor localization using visible light via fusion of multiple classifiers,” IEEE Photonics J. 9(6), 1–16 (2017).
[Crossref]

Son, T. T.

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

Soto, I.

F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
[Crossref]

Syed, Z.

Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
[Crossref]

Thompson, J.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Tsonev, D.

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

Tuan, N. V.

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

Van, M. T.

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

Vellambi, B. N.

Videv, S.

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

Wang, C.

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

Wawrzynski, P.

P. Wawrzyński, “Reinforcement learning with experience replay for model-free humanoid walking optimization,” International Journal of Humanoid Robotics 11(3), 137 (2014).
[Crossref]

Wu, Y.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Yang, C.

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

Yang, J.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

Yasir, M.

Zhang, W.

W. Zhang, M. I. S. Chowdhury, and M. Kavehrad, “Asynchronous indoor positioning system based on visible light communications,” Opt. Eng. 53(4), 045105 (2014).
[Crossref]

Zhuang, Y.

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
[Crossref]

IEEE Comm. Surv. and Tutor. (2)

J. Luo, L. Fan, and H. Li, “Indoor positioning systems based on visible light communication: state of the art,” IEEE Comm. Surv. and Tutor. 19(4), 2871–2893 (2017).
[Crossref]

Y. Zhuang, L. Hua, L. Qi, J. Yang, P. Cao, Y. Cao, Y. Wu, J. Thompson, and H. Haas, “A survey of positioning systems using visible LED lights,” IEEE Comm. Surv. and Tutor. 20(3), 1963–1988 (2018).
[Crossref]

IEEE Commun. Lett. (1)

S. Fang, C. Wang, T. Huang, C. Yang, and Y. Chen, “An enhanced ZigBee indoor positioning system with an ensemble approach,” IEEE Commun. Lett. 16(4), 564–567 (2012).
[Crossref]

IEEE Commun. Mag. (1)

H. Burchardt, N. Serafimovski, D. Tsonev, S. Videv, and H. Haas, “VLC: beyond point-to-point communication,” IEEE Commun. Mag. 52(7), 98–105 (2014).
[Crossref]

IEEE Internet of Things Journal (1)

X. Guo, N. Ansari, L. Li, and H. Li, “Indoor localization by fusing a group of fingerprints based on random forests,” IEEE Internet of Things Journal 5(6), 4686–4698 (2018).
[Crossref]

IEEE Photonics J. (1)

X. Guo, S. Shao, N. Ansari, and A. Khreishah, “Indoor localization using visible light via fusion of multiple classifiers,” IEEE Photonics J. 9(6), 1–16 (2017).
[Crossref]

IEEE Trans. Mobile Comput. (1)

Y. Zhuang, Z. Syed, Y. Li, and N. El-Sheimy, “Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation,” IEEE Trans. Mobile Comput. 15(8), 1982–1995 (2016).
[Crossref]

IET Commun. (2)

F. Seguel, N. Krommenacker, P. Charpentier, and I. Soto, “Visible light positioning based on architecture information: method and performance,” IET Commun. 13(7), 848–856 (2019).
[Crossref]

M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, “Weighted k-nearest neighbour model for indoor VLC positioning,” IET Commun. 11(6), 864–871 (2017).
[Crossref]

International Journal of Humanoid Robotics (1)

P. Wawrzyński, “Reinforcement learning with experience replay for model-free humanoid walking optimization,” International Journal of Humanoid Robotics 11(3), 137 (2014).
[Crossref]

J. Lightwave Technol. (1)

Opt. Eng. (1)

W. Zhang, M. I. S. Chowdhury, and M. Kavehrad, “Asynchronous indoor positioning system based on visible light communications,” Opt. Eng. 53(4), 045105 (2014).
[Crossref]

Sensors (Basel) (1)

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with Bluetooth low energy beacons,” Sensors (Basel) 16(5), 596 (2016).
[Crossref] [PubMed]

Other (9)

H. Hosseinianfar, M. Noshad, and M. Brandt-Pearce, “Positioning for visible light communication system exploiting multipath reflections,” in Proceedings of 2017 IEEE International Conference on Communications (ICC), Paris, 1–6 (2017).
[Crossref]

P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RF-baseduser location and tracking system, ” in Proceedings of IEEE INFOCOM, 775–784 (2000).

X. Li, Y. Cao, and C. Chen, “Machine learning based high accuracy indoor visible light location algorithm,” in 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), Xi'an, 198–203 (2018).
[Crossref]

Y. Liu, K. Park, B. S. Ooi, and M. Alouini, “Indoor localization using three dimensional multi-PDs receiver based on RSS,” in 2018 IEEE Globecom Workshops, Abu Dhabi, United Arab Emirates, 1–6 (2018).

E. Bejar and A. Moran, “Deep reinforcement learning based neuro-control for a two-dimensional magnetic positioning system,” in 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, 268–273 (2018).
[Crossref]

D. Milioris, “Efficient indoor localization via reinforcement learning,” in 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 8350–8354 (2019).
[Crossref]

Z. Zhang, H. Chen, X. Hong, and J. Chen, “Accuracy enhancement of indoor visible light positioning using point-wise reinforcement learning,” in 2019 Optical Fiber Communications Conference and Exposition (OFC), San Diego, California, 1–3 (2019).

C. Hsu, S. Liu, F. Lu, C. Chow, C. Yeh, and G. Chang, “Accurate indoor visible light positioning system utilizing machine learning technique with height tolerance,” in 2018 Optical Fiber Communications Conference and Exposition (OFC), San Diego, California, 1–3 (2018).

J. He, C. Hsu2, Q. Zhou, M. Tang, S. Fu, D. Liu, L. Deng, and G. Chang, “Demonstration of high precision 3D indoor positioning system based on two-layer ANN machine learning technique,” in 2019 Optical Fiber Communications Conference and Exposition (OFC), San Diego, California, 1–3 (2019).

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Figures (8)

Fig. 1
Fig. 1 Schematic diagrams of point-wise reinforcement learning.
Fig. 2
Fig. 2 Schematic diagrams of iterative point-wise reinforcement learning.
Fig. 3
Fig. 3 Experimental setup of the VLP system and the corresponding data processing flow.
Fig. 4
Fig. 4 Sampling points of Detector 1: (a) input for testing different positioning algorithms and (b) training data for the KNN. The 25 red points in (b) coincide with some test points shown in (a).
Fig. 5
Fig. 5 (a) Positioning error versus dis real 12 (cm) for different positioning algorithms; (b) the cumulative distribution function and (c) spatial distribution of the positioning error ( dis real 12 = 40 cm).
Fig. 6
Fig. 6 Spatial distribution of the positioning error when dis real 12 = 40 cm with (a) h0 = 51.05 cm and (b) h0 = 153.05 cm.
Fig. 7
Fig. 7 Cumulative distribution function of the positioning error when dis real 12 = 40 cm with (a) h0 = 51.05 cm and (b) h0 = 153.05 cm.
Fig. 8
Fig. 8 Positioning error versus errors of height difference for the dis real 12 of (a) 40 cm and (b) 30 cm, and (c) the estimated h ^ by the two IPWRL algorithms versus different h0 for the dis real 12 of 40 cm.

Tables (1)

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Table 1 Pseudocode for the IPWRL algorithm.

Equations (12)

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s n ( t ) = i = 1 M ( m + 1 ) A 2 π d n , i 2 cos m ( φ ) cos m ' ( ψ ) β p i ( t - τ i ) + w ( t ) ,
R e c = { peaks of F ( s n ( t ) ) } n = 1 , 2 , , N = [ S 1 ( f 1 ) , , S 1 ( f M ) , , S N ( f 1 ) , , S N ( f M ) ] ,
S n ( f i ) = ( m + 1 ) 2 A 2 β 2 h 2 ( m + m ) 4 π 2 d n , i 2 ( 2 + m + m )
( x n - L i x ) 2 + ( y n - L i y ) 2 + h 2 = d n , i 2 .
d i s r e a l = ( d i s r e a l 12 , d i s r e a l 13 , , d i s r e a l 1 N , , d i s r e a l ( N 1 ) N ) d i s c a l c = ( d i s c a l c 12 , d i s c a l c 13 , , d i s c a l c 1 N , , d i s c a l c ( N 1 ) N ) ,
d i s e r r o r = d i s r e a l d i s calc .
S t a t e = i , i f α i 1 < m a x ( d i s e r r o r ) α i f o r 1 i G ,
R e w a r d = K i K 1 , i f r i 1 < a v e r a g e ( d i s e r r o r ) r i f o r 1 i K ,
R e c n e w _ 1 = [ S 1 ( f 1 ) + s t e p , S 1 ( f 2 ) , , S 1 ( f M ) , S 2 ( f 1 ) , S 2 ( f M ) , , S N ( f 1 ) , , S N ( f M ) ] ,
R e c n e w _ 2 = [ S 1 ( f 1 ) s t e p , S 1 ( f 2 ) , , S 1 ( f M ) , S 2 ( f 1 ) , S 2 ( f M ) , , S N ( f 1 ) , , S N ( f M ) ] ,
h n , i = d ^ n , i 2 ( x n P W R L L i x ) 2 ( y n P W R L L i y ) 2 ,
d ^ n , i 2 = [ ( m + 1 ) 2 A 2 β 2 h 0 2 ( m + m ) 4 π 2 S n ( f i ) ] 1 ( 2 + m + m ) .

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