Abstract

In this work, a novel positioning algorithm based on a long short term memory-fully connected network (LSTM-FCN) is proposed to improve the performance of an indoor visible light positioning (VLP) system. Using the proposed LSTM-FCN based positioning algorithm, the VLP system with a single light emitting diode (LED) and multiple photodetectors (PDs) was implemented. On this basis, the positioning performance of the established VLP system using proposed LSTM-FCN, traditional FCN and support vector regression (SVR) based algorithm is investigated and compared. It is demonstrated that the VLP system using the proposed LSTM-FCN based algorithm has better performance than that using other machine learning algorithms. As a result, an average positioning error of 0.92 cm and a maximum positioning error of less than 5 cm can be obtained for the established VLP system.

© 2021 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

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References

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    [Crossref]

2021 (7)

S. M. Sheikh, H. M. Asif, K. Raahemifar, and F. Al-Turjman, “Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication,” IEEE Access. 9, 52113–52124 (2021).
[Crossref]

D. C. Lin, C. W. Chow, C. W. Peng, T. Y. Hung, Y. H. Chang, S. H. Song, Y. S. Lin, Y. Liu, and K. H. Lin, “Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP),” J. Lightwave Technol. 39(20), 6366–6372 (2021).
[Crossref]

A. H. A. Bakar, T. Glass, H. Y. Tee, F. Alam, and M. Legg, “Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning,” IEEE Trans. Instrum. Meas. 70, 1–12 (2021).
[Crossref]

Z. Wu, D. Rincon, J. W. Luo, and P. D. Christofides, “Machine learning modeling and predictive control of nonlinear processes using noisy data,” AICHE J. 67(4), 1929 (2021).
[Crossref]

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
[Crossref]

I. M. Abou-Shehada, A. F. AlMuallim, A. K. AlFaqeh, A. H. Muqaibel, K. H. Park, and M. S. Alouini, “Accurate Indoor Visible Light Positioning Using a Modified Pathloss Model With Sparse Fingerprints,” J. Lightwave Technol. 39(20), 6487–6497 (2021).
[Crossref]

L. Qin, B. Niu, B. S. Li, X. Li. Hu, and Y. X. Du, “High precision indoor positioning algorithm of single LED lamp based on A-Bayes,” Optik 241, 167190 (2021).
[Crossref]

2020 (10)

X. Y. Wang, Z. Yu, and S. Mao, “Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach,” Mobile Netw Appl. 25(2), 819–832 (2020).
[Crossref]

M. Qiao, H. Yan, X. X. Tang, and C. K. Xu, “Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads,” IEEE Access. 8, 66257–66269 (2020).
[Crossref]

W. Han, J. P. Wang, H. M. Lu, and D. Y. Chen, “Visible light indoor positioning via an iterative algorithm based on an m5 model tree,” Appl. Opt. 59(32), 10194 (2020).
[Crossref]

Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
[Crossref]

N. Huang, C. Gong, J. Luo, and Z. Xu, “Design and Demonstration of Robust Visible Light Positioning Based on Received Signal Strength,” J. Lightwave Technol. 38(20), 5695–5707 (2020).
[Crossref]

A. A. Mahmoud, Z. U. Ahmad, C. L. Haas, and S. Rajbhandari, “Precision indoor three-dimensional visible light positioning using receiver diversity and multi-layer perceptron neural network,” IET Optoelectron. 14(6), 440–446 (2020).
[Crossref]

Y. Chen, W. P. Guan, J. Y. Li, and H. Z. Song, “Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine,” IEEE Access. 8, 13875–13886 (2020).
[Crossref]

M. H. Rahman, M. A. S. Sejan, J. J. Kim, and W. Y. Chung, “Reduced tilting effect of smartphone cmos image sensor in visible light indoor positioning,” Electronics. 9(10), 1635 (2020).
[Crossref]

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
[Crossref]

A. Chaabna, A. Babouri, X. Zhang, C. Huang, and H. Chouabia, “New indoor positioning technique using spectral data compression based on VLC for performance improvement,” Opt Quant Electron 52(7), 343 (2020).
[Crossref]

2019 (9)

H. Q. Tran and C. Ha, “Fingerprint-Based Indoor Positioning System Using Visible Light Communication-A Novel Method for Multipath Reflections,” Electronics. 8(1), 63 (2019).
[Crossref]

H. Q. Tran and C. Ha, “Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression,” Appl. Sci. 9(6), 1048 (2019).
[Crossref]

F. Alam, M. T. Chew, T. Wenge, and G. S. Gupta, “An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model,” IEEE Trans. Instrum. Meas. 68(8), 2714–2723 (2019).
[Crossref]

Z. Zhang, Y. G. Zhu, W. T. Zhu, H. Y. Chen, X. Z. Hong, and J. J. Chen, “Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning,” Opt. Express 27(16), 22161–22172 (2019).
[Crossref]

B. P. Zhou, A. Liu, and V. K. Lau, “Joint User Location and Orientation Estimation for Visible Light Communication Systems With Unknown Power Emission,” IEEE Trans. Wirel. Commun. 18(11), 5181–5195 (2019).
[Crossref]

J. Hao, J. Chen, and R. Wang, “Visible Light Positioning Using A Single LED Luminaire,” IEEE Photonics J. 11(5), 1–13 (2019).
[Crossref]

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

G. Hussain, M. S. Jabbar, J. D. Cho, and S. Bae, “Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification,” Electronics. 8(4), 375 (2019).
[Crossref]

H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
[Crossref]

2018 (5)

T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
[Crossref]

X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

X. H. Yu, J. P. Wang, and H. M. Lu, “Single LED-Based Indoor Positioning System Using Multiple Photodetectors,” IEEE Photonics J. 10(6), 1–8 (2018).
[Crossref]

W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
[Crossref]

I. G. Alonsogonzalez, D. Sanchezrodriguez, C. Leybosch, and M. A. QuintanaSuárez, “Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication,” Sensors. 18(4), 1040 (2018).
[Crossref]

2016 (1)

Y. N. Hou, S. L. Xiao, M. H. Bi, Y. K. Xue, W. S. Pan, and W. S. Hu, “Single LED Beacon-Based 3-D Indoor Positioning Using Off-the-Shelf Devices,” IEEE Photonics J. 8(6), 1–11 (2016).
[Crossref]

2015 (1)

J. Torressospedra, R. Montoliu, S. Trilles, Óscar Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Syst. Appl. 42(23), 9263–9278 (2015).
[Crossref]

2008 (1)

D. Dardari, A. Conti, J. Lien, C. Andrea, and Z. W. Moe, “The effect of cooperation on UWB-based positioning systems using experimental data,” EURASIP J Adv Signal Process. 2008(1), 513873 (2008).
[Crossref]

2004 (1)

T. Komine and M. Nakagawa, “Fundamental analysis for visible-light communication system using LED lights,” IEEE Trans. Consumer Electron. 50(1), 100–107 (2004).
[Crossref]

2000 (1)

O. L. Mangasarian and D. R. Musicant, “Robust linear and support vector regression,” IEEE Trans. Pattern Anal. Machine Intell 22(9), 950–955 (2000).
[Crossref]

Abou-Shehada, I. M.

Ahmad, T.

M. Saadi, Z. Saeed, T. Ahmad, M. K. Saleem, and L. Wuttisittikulkij, “Visible light-based indoor localization using k-means clustering and linear regression,” in “transactions on emerging telecommunications technologies,” (2019), pp.11–12.

Ahmad, Z. U.

A. A. Mahmoud, Z. U. Ahmad, C. L. Haas, and S. Rajbhandari, “Precision indoor three-dimensional visible light positioning using receiver diversity and multi-layer perceptron neural network,” IET Optoelectron. 14(6), 440–446 (2020).
[Crossref]

Akiyama, T.

T. Akiyama, M. Sugimoto, and H. Hashizume, “Time-of-arrival-based smartphone localization using visible light communication,” in “international conference on indoor positioning and indoor navigation,” (2017), pp.1–7.

Alam, F.

A. H. A. Bakar, T. Glass, H. Y. Tee, F. Alam, and M. Legg, “Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning,” IEEE Trans. Instrum. Meas. 70, 1–12 (2021).
[Crossref]

F. Alam, M. T. Chew, T. Wenge, and G. S. Gupta, “An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model,” IEEE Trans. Instrum. Meas. 68(8), 2714–2723 (2019).
[Crossref]

AlFaqeh, A. K.

AlMuallim, A. F.

Alonsogonzalez, I. G.

I. G. Alonsogonzalez, D. Sanchezrodriguez, C. Leybosch, and M. A. QuintanaSuárez, “Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication,” Sensors. 18(4), 1040 (2018).
[Crossref]

Alouini, M. S.

Alphones, A.

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

Al-Turjman, F.

S. M. Sheikh, H. M. Asif, K. Raahemifar, and F. Al-Turjman, “Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication,” IEEE Access. 9, 52113–52124 (2021).
[Crossref]

Amini, C.

C. Amini, A. Taherpour, T. Khattab, and S. Gazor, “Theoretical accuracy analysis of indoor visible light communication positioning system based on time-of-arrival,” in “canadian conference on electrical and computer engineering,” (2016), pp.1–5.

Aminikashani,

Kavehrad, R. Mohsen, and Aminikashani, “Impact of Multipath Reflections. Visible Light Communication Based Indoor Localization,” 2019.

Andrea, C.

D. Dardari, A. Conti, J. Lien, C. Andrea, and Z. W. Moe, “The effect of cooperation on UWB-based positioning systems using experimental data,” EURASIP J Adv Signal Process. 2008(1), 513873 (2008).
[Crossref]

Asif, H. M.

S. M. Sheikh, H. M. Asif, K. Raahemifar, and F. Al-Turjman, “Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication,” IEEE Access. 9, 52113–52124 (2021).
[Crossref]

Atif, K.

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
[Crossref]

Baadji, B.

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
[Crossref]

Babouri, A.

A. Chaabna, A. Babouri, X. Zhang, C. Huang, and H. Chouabia, “New indoor positioning technique using spectral data compression based on VLC for performance improvement,” Opt Quant Electron 52(7), 343 (2020).
[Crossref]

Bae, S.

G. Hussain, M. S. Jabbar, J. D. Cho, and S. Bae, “Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification,” Electronics. 8(4), 375 (2019).
[Crossref]

Bakar, A. H. A.

A. H. A. Bakar, T. Glass, H. Y. Tee, F. Alam, and M. Legg, “Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning,” IEEE Trans. Instrum. Meas. 70, 1–12 (2021).
[Crossref]

Bakdi, A.

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
[Crossref]

Bali, N.

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
[Crossref]

Belagoune, S.

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
[Crossref]

Belmonte, Óscar

J. Torressospedra, R. Montoliu, S. Trilles, Óscar Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Syst. Appl. 42(23), 9263–9278 (2015).
[Crossref]

Bi, M. H.

Y. N. Hou, S. L. Xiao, M. H. Bi, Y. K. Xue, W. S. Pan, and W. S. Hu, “Single LED Beacon-Based 3-D Indoor Positioning Using Off-the-Shelf Devices,” IEEE Photonics J. 8(6), 1–11 (2016).
[Crossref]

Chaabna, A.

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Chen, H. Y.

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J. Hao, J. Chen, and R. Wang, “Visible Light Positioning Using A Single LED Luminaire,” IEEE Photonics J. 11(5), 1–13 (2019).
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T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
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W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
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G. Hussain, M. S. Jabbar, J. D. Cho, and S. Bae, “Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification,” Electronics. 8(4), 375 (2019).
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A. Chaabna, A. Babouri, X. Zhang, C. Huang, and H. Chouabia, “New indoor positioning technique using spectral data compression based on VLC for performance improvement,” Opt Quant Electron 52(7), 343 (2020).
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M. H. Rahman, M. A. S. Sejan, J. J. Kim, and W. Y. Chung, “Reduced tilting effect of smartphone cmos image sensor in visible light indoor positioning,” Electronics. 9(10), 1635 (2020).
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H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
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D. Dardari, A. Conti, J. Lien, C. Andrea, and Z. W. Moe, “The effect of cooperation on UWB-based positioning systems using experimental data,” EURASIP J Adv Signal Process. 2008(1), 513873 (2008).
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L. Qin, B. Niu, B. S. Li, X. Li. Hu, and Y. X. Du, “High precision indoor positioning algorithm of single LED lamp based on A-Bayes,” Optik 241, 167190 (2021).
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Guan, W. P.

Y. Chen, W. P. Guan, J. Y. Li, and H. Z. Song, “Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine,” IEEE Access. 8, 13875–13886 (2020).
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W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
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F. Alam, M. T. Chew, T. Wenge, and G. S. Gupta, “An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model,” IEEE Trans. Instrum. Meas. 68(8), 2714–2723 (2019).
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T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
[Crossref]

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Hao, J.

J. Hao, J. Chen, and R. Wang, “Visible Light Positioning Using A Single LED Luminaire,” IEEE Photonics J. 11(5), 1–13 (2019).
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X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
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A. Naeem A, N. U. Hassa, M. A. Pasha, C. Yuen, and A. Sikora, “Performance analysis of TDOA-based indoor positioning systems using visible LED lights,” in “2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS),” (2018), pp.103–107.

Hong, C. Y.

Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
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X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
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L. Qin, B. Niu, B. S. Li, X. Li. Hu, and Y. X. Du, “High precision indoor positioning algorithm of single LED lamp based on A-Bayes,” Optik 241, 167190 (2021).
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A. Chaabna, A. Babouri, X. Zhang, C. Huang, and H. Chouabia, “New indoor positioning technique using spectral data compression based on VLC for performance improvement,” Opt Quant Electron 52(7), 343 (2020).
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H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
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H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
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W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
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Hussain, G.

G. Hussain, M. S. Jabbar, J. D. Cho, and S. Bae, “Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification,” Electronics. 8(4), 375 (2019).
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G. Hussain, M. S. Jabbar, J. D. Cho, and S. Bae, “Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification,” Electronics. 8(4), 375 (2019).
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Kavehrad, R. Mohsen, and Aminikashani, “Impact of Multipath Reflections. Visible Light Communication Based Indoor Localization,” 2019.

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Z. Cui, R. Ke, and Y. H. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” arXiv:1801.02143 [cs.LG] (2018).

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C. Amini, A. Taherpour, T. Khattab, and S. Gazor, “Theoretical accuracy analysis of indoor visible light communication positioning system based on time-of-arrival,” in “canadian conference on electrical and computer engineering,” (2016), pp.1–5.

Kim, J. J.

M. H. Rahman, M. A. S. Sejan, J. J. Kim, and W. Y. Chung, “Reduced tilting effect of smartphone cmos image sensor in visible light indoor positioning,” Electronics. 9(10), 1635 (2020).
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T. Komine and M. Nakagawa, “Fundamental analysis for visible-light communication system using LED lights,” IEEE Trans. Consumer Electron. 50(1), 100–107 (2004).
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B. P. Zhou, A. Liu, and V. K. Lau, “Joint User Location and Orientation Estimation for Visible Light Communication Systems With Unknown Power Emission,” IEEE Trans. Wirel. Commun. 18(11), 5181–5195 (2019).
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H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
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I. G. Alonsogonzalez, D. Sanchezrodriguez, C. Leybosch, and M. A. QuintanaSuárez, “Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication,” Sensors. 18(4), 1040 (2018).
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L. Qin, B. Niu, B. S. Li, X. Li. Hu, and Y. X. Du, “High precision indoor positioning algorithm of single LED lamp based on A-Bayes,” Optik 241, 167190 (2021).
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H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
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Y. Chen, W. P. Guan, J. Y. Li, and H. Z. Song, “Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine,” IEEE Access. 8, 13875–13886 (2020).
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D. Dardari, A. Conti, J. Lien, C. Andrea, and Z. W. Moe, “The effect of cooperation on UWB-based positioning systems using experimental data,” EURASIP J Adv Signal Process. 2008(1), 513873 (2008).
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H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
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D. C. Lin, C. W. Chow, C. W. Peng, T. Y. Hung, Y. H. Chang, S. H. Song, Y. S. Lin, Y. Liu, and K. H. Lin, “Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP),” J. Lightwave Technol. 39(20), 6366–6372 (2021).
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Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
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Lin, P. X.

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
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D. C. Lin, C. W. Chow, C. W. Peng, T. Y. Hung, Y. H. Chang, S. H. Song, Y. S. Lin, Y. Liu, and K. H. Lin, “Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP),” J. Lightwave Technol. 39(20), 6366–6372 (2021).
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Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
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B. P. Zhou, A. Liu, and V. K. Lau, “Joint User Location and Orientation Estimation for Visible Light Communication Systems With Unknown Power Emission,” IEEE Trans. Wirel. Commun. 18(11), 5181–5195 (2019).
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Liu, Y.

D. C. Lin, C. W. Chow, C. W. Peng, T. Y. Hung, Y. H. Chang, S. H. Song, Y. S. Lin, Y. Liu, and K. H. Lin, “Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP),” J. Lightwave Technol. 39(20), 6366–6372 (2021).
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Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
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Liu, Z. X.

W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
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Luo, J. W.

Z. Wu, D. Rincon, J. W. Luo, and P. D. Christofides, “Machine learning modeling and predictive control of nonlinear processes using noisy data,” AICHE J. 67(4), 1929 (2021).
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H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
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Kavehrad, R. Mohsen, and Aminikashani, “Impact of Multipath Reflections. Visible Light Communication Based Indoor Localization,” 2019.

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J. Torressospedra, R. Montoliu, S. Trilles, Óscar Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Syst. Appl. 42(23), 9263–9278 (2015).
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T. Komine and M. Nakagawa, “Fundamental analysis for visible-light communication system using LED lights,” IEEE Trans. Consumer Electron. 50(1), 100–107 (2004).
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Y. N. Hou, S. L. Xiao, M. H. Bi, Y. K. Xue, W. S. Pan, and W. S. Hu, “Single LED Beacon-Based 3-D Indoor Positioning Using Off-the-Shelf Devices,” IEEE Photonics J. 8(6), 1–11 (2016).
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A. Naeem A, N. U. Hassa, M. A. Pasha, C. Yuen, and A. Sikora, “Performance analysis of TDOA-based indoor positioning systems using visible LED lights,” in “2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS),” (2018), pp.103–107.

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I. G. Alonsogonzalez, D. Sanchezrodriguez, C. Leybosch, and M. A. QuintanaSuárez, “Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication,” Sensors. 18(4), 1040 (2018).
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A. A. Mahmoud, Z. U. Ahmad, C. L. Haas, and S. Rajbhandari, “Precision indoor three-dimensional visible light positioning using receiver diversity and multi-layer perceptron neural network,” IET Optoelectron. 14(6), 440–446 (2020).
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[Crossref]

Saadi, M.

M. Saadi, Z. Saeed, T. Ahmad, M. K. Saleem, and L. Wuttisittikulkij, “Visible light-based indoor localization using k-means clustering and linear regression,” in “transactions on emerging telecommunications technologies,” (2019), pp.11–12.

Saeed, Z.

M. Saadi, Z. Saeed, T. Ahmad, M. K. Saleem, and L. Wuttisittikulkij, “Visible light-based indoor localization using k-means clustering and linear regression,” in “transactions on emerging telecommunications technologies,” (2019), pp.11–12.

Saleem, M. K.

M. Saadi, Z. Saeed, T. Ahmad, M. K. Saleem, and L. Wuttisittikulkij, “Visible light-based indoor localization using k-means clustering and linear regression,” in “transactions on emerging telecommunications technologies,” (2019), pp.11–12.

Sanchezrodriguez, D.

I. G. Alonsogonzalez, D. Sanchezrodriguez, C. Leybosch, and M. A. QuintanaSuárez, “Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication,” Sensors. 18(4), 1040 (2018).
[Crossref]

Sejan, M. A. S.

M. H. Rahman, M. A. S. Sejan, J. J. Kim, and W. Y. Chung, “Reduced tilting effect of smartphone cmos image sensor in visible light indoor positioning,” Electronics. 9(10), 1635 (2020).
[Crossref]

Sheikh, S. M.

S. M. Sheikh, H. M. Asif, K. Raahemifar, and F. Al-Turjman, “Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication,” IEEE Access. 9, 52113–52124 (2021).
[Crossref]

Shen, Y.

X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

Sikora, A.

A. Naeem A, N. U. Hassa, M. A. Pasha, C. Yuen, and A. Sikora, “Performance analysis of TDOA-based indoor positioning systems using visible LED lights,” in “2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS),” (2018), pp.103–107.

Song, H. Z.

Y. Chen, W. P. Guan, J. Y. Li, and H. Z. Song, “Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine,” IEEE Access. 8, 13875–13886 (2020).
[Crossref]

Song, S. H.

D. C. Lin, C. W. Chow, C. W. Peng, T. Y. Hung, Y. H. Chang, S. H. Song, Y. S. Lin, Y. Liu, and K. H. Lin, “Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP),” J. Lightwave Technol. 39(20), 6366–6372 (2021).
[Crossref]

Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
[Crossref]

Sugimoto, M.

T. Akiyama, M. Sugimoto, and H. Hashizume, “Time-of-arrival-based smartphone localization using visible light communication,” in “international conference on indoor positioning and indoor navigation,” (2017), pp.1–7.

Taherpour, A.

C. Amini, A. Taherpour, T. Khattab, and S. Gazor, “Theoretical accuracy analysis of indoor visible light communication positioning system based on time-of-arrival,” in “canadian conference on electrical and computer engineering,” (2016), pp.1–5.

Tang, X. X.

M. Qiao, H. Yan, X. X. Tang, and C. K. Xu, “Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads,” IEEE Access. 8, 66257–66269 (2020).
[Crossref]

Tee, H. Y.

A. H. A. Bakar, T. Glass, H. Y. Tee, F. Alam, and M. Legg, “Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning,” IEEE Trans. Instrum. Meas. 70, 1–12 (2021).
[Crossref]

Torressospedra, J.

J. Torressospedra, R. Montoliu, S. Trilles, Óscar Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Syst. Appl. 42(23), 9263–9278 (2015).
[Crossref]

Tran, H. Q.

H. Q. Tran and C. Ha, “Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression,” Appl. Sci. 9(6), 1048 (2019).
[Crossref]

H. Q. Tran and C. Ha, “Fingerprint-Based Indoor Positioning System Using Visible Light Communication-A Novel Method for Multipath Reflections,” Electronics. 8(1), 63 (2019).
[Crossref]

Trilles, S.

J. Torressospedra, R. Montoliu, S. Trilles, Óscar Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Syst. Appl. 42(23), 9263–9278 (2015).
[Crossref]

Wang, J. P.

W. Han, J. P. Wang, H. M. Lu, and D. Y. Chen, “Visible light indoor positioning via an iterative algorithm based on an m5 model tree,” Appl. Opt. 59(32), 10194 (2020).
[Crossref]

X. H. Yu, J. P. Wang, and H. M. Lu, “Single LED-Based Indoor Positioning System Using Multiple Photodetectors,” IEEE Photonics J. 10(6), 1–8 (2018).
[Crossref]

Wang, K.

X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

Wang, R.

J. Hao, J. Chen, and R. Wang, “Visible Light Positioning Using A Single LED Luminaire,” IEEE Photonics J. 11(5), 1–13 (2019).
[Crossref]

Wang, X. Y.

X. Y. Wang, Z. Yu, and S. Mao, “Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach,” Mobile Netw Appl. 25(2), 819–832 (2020).
[Crossref]

Wang, Y.

X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
[Crossref]

Wang, Y. H.

Z. Cui, R. Ke, and Y. H. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” arXiv:1801.02143 [cs.LG] (2018).

Wei, Z. H.

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
[Crossref]

Wenge, T.

F. Alam, M. T. Chew, T. Wenge, and G. S. Gupta, “An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model,” IEEE Trans. Instrum. Meas. 68(8), 2714–2723 (2019).
[Crossref]

Wu, H.

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
[Crossref]

Wu, Y. C.

Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
[Crossref]

Wu, Y. X.

W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
[Crossref]

Wu, Z.

Z. Wu, D. Rincon, J. W. Luo, and P. D. Christofides, “Machine learning modeling and predictive control of nonlinear processes using noisy data,” AICHE J. 67(4), 1929 (2021).
[Crossref]

Wuttisittikulkij, L.

M. Saadi, Z. Saeed, T. Ahmad, M. K. Saleem, and L. Wuttisittikulkij, “Visible light-based indoor localization using k-means clustering and linear regression,” in “transactions on emerging telecommunications technologies,” (2019), pp.11–12.

Xiao, S. L.

Y. N. Hou, S. L. Xiao, M. H. Bi, Y. K. Xue, W. S. Pan, and W. S. Hu, “Single LED Beacon-Based 3-D Indoor Positioning Using Off-the-Shelf Devices,” IEEE Photonics J. 8(6), 1–11 (2016).
[Crossref]

Xu, C. K.

M. Qiao, H. Yan, X. X. Tang, and C. K. Xu, “Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads,” IEEE Access. 8, 66257–66269 (2020).
[Crossref]

Xu, Y.

T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
[Crossref]

Xu, Y. Z.

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
[Crossref]

Xu, Z.

Xue, Y. K.

Y. N. Hou, S. L. Xiao, M. H. Bi, Y. K. Xue, W. S. Pan, and W. S. Hu, “Single LED Beacon-Based 3-D Indoor Positioning Using Off-the-Shelf Devices,” IEEE Photonics J. 8(6), 1–11 (2016).
[Crossref]

Yan, H.

M. Qiao, H. Yan, X. X. Tang, and C. K. Xu, “Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads,” IEEE Access. 8, 66257–66269 (2020).
[Crossref]

Yang, A. Y.

H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
[Crossref]

Yang, H. L.

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

Yang, Y. B.

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

Yeh, C. H.

Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
[Crossref]

Yu, X. H.

X. H. Yu, J. P. Wang, and H. M. Lu, “Single LED-Based Indoor Positioning System Using Multiple Photodetectors,” IEEE Photonics J. 10(6), 1–8 (2018).
[Crossref]

Yu, Z.

X. Y. Wang, Z. Yu, and S. Mao, “Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach,” Mobile Netw Appl. 25(2), 819–832 (2020).
[Crossref]

Yuan, S. C.

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
[Crossref]

Yuan, T.

T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
[Crossref]

Yuen, C.

A. Naeem A, N. U. Hassa, M. A. Pasha, C. Yuen, and A. Sikora, “Performance analysis of TDOA-based indoor positioning systems using visible LED lights,” in “2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS),” (2018), pp.103–107.

Zhang, H. Q.

H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
[Crossref]

Zhang, R.

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

Zhang, S.

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

Zhang, X.

A. Chaabna, A. Babouri, X. Zhang, C. Huang, and H. Chouabia, “New indoor positioning technique using spectral data compression based on VLC for performance improvement,” Opt Quant Electron 52(7), 343 (2020).
[Crossref]

X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

Zhang, Z.

Zhong, W. D.

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

Zhou, B. P.

B. P. Zhou, A. Liu, and V. K. Lau, “Joint User Location and Orientation Estimation for Visible Light Communication Systems With Unknown Power Emission,” IEEE Trans. Wirel. Commun. 18(11), 5181–5195 (2019).
[Crossref]

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Zhu, Y. G.

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X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

AICHE J. (1)

Z. Wu, D. Rincon, J. W. Luo, and P. D. Christofides, “Machine learning modeling and predictive control of nonlinear processes using noisy data,” AICHE J. 67(4), 1929 (2021).
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Appl. Opt. (1)

Appl. Sci. (1)

H. Q. Tran and C. Ha, “Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression,” Appl. Sci. 9(6), 1048 (2019).
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Applied Acoustics (1)

X. Zhang, Z. X. Zou, K. Wang, Q. S. Hao, Y. Wang, Y. Shen, and H. S. Hu, “A new rail crack detection method using LSTM network for actual application based on AE technology,” Applied Acoustics 142(15), 78–86 (2018).
[Crossref]

Electronics. (3)

H. Q. Tran and C. Ha, “Fingerprint-Based Indoor Positioning System Using Visible Light Communication-A Novel Method for Multipath Reflections,” Electronics. 8(1), 63 (2019).
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G. Hussain, M. S. Jabbar, J. D. Cho, and S. Bae, “Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification,” Electronics. 8(4), 375 (2019).
[Crossref]

M. H. Rahman, M. A. S. Sejan, J. J. Kim, and W. Y. Chung, “Reduced tilting effect of smartphone cmos image sensor in visible light indoor positioning,” Electronics. 9(10), 1635 (2020).
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J. Torressospedra, R. Montoliu, S. Trilles, Óscar Belmonte, and J. Huerta, “Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems,” Expert Syst. Appl. 42(23), 9263–9278 (2015).
[Crossref]

IEEE Access (2)

Y. C. Wu, C. W. Chow, Y. Liu, Y. S. Lin, C. Y. Hong, D. C. Lin, S. H. Song, and C. H. Yeh, “Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method,” IEEE Access 8, 214269–214281 (2020).
[Crossref]

P. F. Du, S. Zhang, C. Chen, H. L. Yang, W. D. Zhong, R. Zhang, A. Alphones, and Y. B. Yang, “Experimental Demonstration of 3D Visible Light Positioning Using Received Signal Strength With Low-Complexity Trilateration Assisted by Deep Learning Technique,” IEEE Access 7, 93986–93997 (2019).
[Crossref]

IEEE Access. (3)

M. Qiao, H. Yan, X. X. Tang, and C. K. Xu, “Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads,” IEEE Access. 8, 66257–66269 (2020).
[Crossref]

Y. Chen, W. P. Guan, J. Y. Li, and H. Z. Song, “Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine,” IEEE Access. 8, 13875–13886 (2020).
[Crossref]

S. M. Sheikh, H. M. Asif, K. Raahemifar, and F. Al-Turjman, “Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication,” IEEE Access. 9, 52113–52124 (2021).
[Crossref]

IEEE Photonics J. (7)

W. P. Guan, X. Chen, M. X. Huang, Z. X. Liu, Y. X. Wu, and Y. C. Chen, “High-Speed Robust Dynamic Positioning and Tracking Method Based on Visual Visible Light Communication Using Optical Flow Detection and Bayesian Forecast,” IEEE Photonics J. 10(3), 1–22 (2018).
[Crossref]

J. Hao, J. Chen, and R. Wang, “Visible Light Positioning Using A Single LED Luminaire,” IEEE Photonics J. 11(5), 1–13 (2019).
[Crossref]

X. H. Yu, J. P. Wang, and H. M. Lu, “Single LED-Based Indoor Positioning System Using Multiple Photodetectors,” IEEE Photonics J. 10(6), 1–8 (2018).
[Crossref]

Y. N. Hou, S. L. Xiao, M. H. Bi, Y. K. Xue, W. S. Pan, and W. S. Hu, “Single LED Beacon-Based 3-D Indoor Positioning Using Off-the-Shelf Devices,” IEEE Photonics J. 8(6), 1–11 (2016).
[Crossref]

H. P. Li, H. B. Huang, Y. Z. Xu, Z. H. Wei, S. C. Yuan, P. X. Lin, H. Wu, W. Lei, J. B. Fang, and Z. Chen, “A Fast and High-Accuracy Real-Time Visible Light Positioning System Based on Single LED Lamp With a Beacon,” IEEE Photonics J. 12(6), 1–12 (2020).
[Crossref]

H. Q. Zhang, J. H. Cui, L. H. Feng, A. Y. Yang, H. C. Lv, B. Lin, and H. Q. Huang, “High-Precision Indoor Visible Light Positioning Using Deep Neural Network Based on the Bayesian Regularization With Sparse Training Point,” IEEE Photonics J. 11(3), 1–10 (2019).
[Crossref]

T. Yuan, Y. Xu, Y. Wang, P. Han, and J. F. Chen, “A Tilt Receiver Correction Method for Visible Light Positioning Using Machine Learning Method,” IEEE Photonics J. 10(6), 1–12 (2018).
[Crossref]

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A. H. A. Bakar, T. Glass, H. Y. Tee, F. Alam, and M. Legg, “Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning,” IEEE Trans. Instrum. Meas. 70, 1–12 (2021).
[Crossref]

F. Alam, M. T. Chew, T. Wenge, and G. S. Gupta, “An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model,” IEEE Trans. Instrum. Meas. 68(8), 2714–2723 (2019).
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B. P. Zhou, A. Liu, and V. K. Lau, “Joint User Location and Orientation Estimation for Visible Light Communication Systems With Unknown Power Emission,” IEEE Trans. Wirel. Commun. 18(11), 5181–5195 (2019).
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A. A. Mahmoud, Z. U. Ahmad, C. L. Haas, and S. Rajbhandari, “Precision indoor three-dimensional visible light positioning using receiver diversity and multi-layer perceptron neural network,” IET Optoelectron. 14(6), 440–446 (2020).
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J. Lightwave Technol. (3)

Measurement. (1)

S. Belagoune, N. Bali, A. Bakdi, B. Baadji, and K. Atif, “Deep Learning through LSTM Classification and Regression for Transmission Line Fault Detection, Diagnosis and Location in Large-Scale Multi-Machine Power Systems,” Measurement. 177, 109330 (2021).
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Mobile Netw Appl. (1)

X. Y. Wang, Z. Yu, and S. Mao, “Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach,” Mobile Netw Appl. 25(2), 819–832 (2020).
[Crossref]

Opt Quant Electron (1)

A. Chaabna, A. Babouri, X. Zhang, C. Huang, and H. Chouabia, “New indoor positioning technique using spectral data compression based on VLC for performance improvement,” Opt Quant Electron 52(7), 343 (2020).
[Crossref]

Opt. Express (1)

Optik (1)

L. Qin, B. Niu, B. S. Li, X. Li. Hu, and Y. X. Du, “High precision indoor positioning algorithm of single LED lamp based on A-Bayes,” Optik 241, 167190 (2021).
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Sensors. (1)

I. G. Alonsogonzalez, D. Sanchezrodriguez, C. Leybosch, and M. A. QuintanaSuárez, “Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication,” Sensors. 18(4), 1040 (2018).
[Crossref]

Other (6)

M. Saadi, Z. Saeed, T. Ahmad, M. K. Saleem, and L. Wuttisittikulkij, “Visible light-based indoor localization using k-means clustering and linear regression,” in “transactions on emerging telecommunications technologies,” (2019), pp.11–12.

A. Naeem A, N. U. Hassa, M. A. Pasha, C. Yuen, and A. Sikora, “Performance analysis of TDOA-based indoor positioning systems using visible LED lights,” in “2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS),” (2018), pp.103–107.

Kavehrad, R. Mohsen, and Aminikashani, “Impact of Multipath Reflections. Visible Light Communication Based Indoor Localization,” 2019.

T. Akiyama, M. Sugimoto, and H. Hashizume, “Time-of-arrival-based smartphone localization using visible light communication,” in “international conference on indoor positioning and indoor navigation,” (2017), pp.1–7.

C. Amini, A. Taherpour, T. Khattab, and S. Gazor, “Theoretical accuracy analysis of indoor visible light communication positioning system based on time-of-arrival,” in “canadian conference on electrical and computer engineering,” (2016), pp.1–5.

Z. Cui, R. Ke, and Y. H. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” arXiv:1801.02143 [cs.LG] (2018).

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (10)

Fig. 1.
Fig. 1. The structure of LSTM-FCN used in this work for the VLP system.
Fig. 2.
Fig. 2. The structure of the established VLP system using LSTM-FCN.
Fig. 3.
Fig. 3. Data set.
Fig. 4.
Fig. 4. Noise characteristics of the established VLP system: (a) power spectral density; (b) the distribution characteristics of the amplitude.
Fig. 5.
Fig. 5. Training losses and CDF of localization errors for different input sequence length. (a) x-axis, (b) y-axis.
Fig. 6.
Fig. 6. Error across the different axis. (a) Mean positioning error, (b) CDF.
Fig. 7.
Fig. 7. The error distribution of positioning results in the location plane for the established VLP system using (a) LSTM-FCN, (b) FCN and (c) SVR model.
Fig. 8.
Fig. 8. Positioning accuracy comparison for the established VLP system using different algorithms. (a) BOX, (b) CDF.
Fig. 9.
Fig. 9. FCN and SVR with simple denoising methods. (a) mean position value, (b) mean received power.
Fig. 10.
Fig. 10. The positioning results in the location plane for the established VLP system using (a) LSTM-FCN, (b) FCN and (c) SVR algorithm.

Tables (2)

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Table 1. System parameters.

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Table 2. Comparative study of the proposed system with published work.

Equations (8)

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p i j = [ p i j 1 p i j 2 p i j 3 ] 1 i m , 1 j n ,
p i j k = [ p i j k , 1 p i j k , 2 p i j k , t ] k = 1 , 2 , 3 ,
T r a i n X i j = [ p i j x i j ] = [ p i j 1 , 1 p i j 1 , t p i j 2 , 1 p i j 2 , t p i j 3 , 1 p i j 3 , t x i j ] ,
e r r o r = ( x p x ) 2 + ( y p y ) 2 ,
r ( t ) = x ( t ) ( H L O S + H N L O S ) + n ( t ) ,
H L O S = { ( n + 1 2 π ) ( A l 2 ) cos α ( ϕ 1 ) T s ( ψ d ) G ( ψ d ) cos ( ψ d ) , 0 ψ d FOV 0 , ψ d > FOV ,
H N L O S = { ( n + 1 2 π 2 ) ( A l 1 2 l 2 2 ) ρ cos α ( ϕ 2 ) d A r e f cos ( λ ) cos ( γ ) cos ( ψ r ) T s ( ψ r ) G ( ψ r ) , 0 ψ r FOV 0 , ψ r > FOV ,
N x y = R x y 1 T T r x y , t .

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