Volume 33 Issue 1
Jan.  2024
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Li ZHAO, Yi REN, Qi WANG, et al., “Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 195–203, 2024 doi: 10.23919/cje.2022.00.161
Citation: Li ZHAO, Yi REN, Qi WANG, et al., “Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 195–203, 2024 doi: 10.23919/cje.2022.00.161

Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network

doi: 10.23919/cje.2022.00.161
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  • Author Bio:

    Li ZHAO Professor, Master Supervisor, Xi’an Technology University. Her main research interests include optical channel characteristics, channel coding, modulation technology, signal processing in free space optical communication system; light source layout, modulation technology, indoor positioning in visible light communication system. As the project leader, she has undertaken more than ten scientific research projects in the field of optical communication. As a main participant, she has participated in many scientific research projects. She has obtained three national invention patents, one national utility model patent and two software registration rights. She has published more than 50 papers in the field of optical communication. (Email: pilly_lily@126.com)

  • Corresponding author: Email: pilly_lily@126.com
  • Received Date: 2022-06-12
  • Accepted Date: 2022-07-30
  • Available Online: 2022-11-05
  • Publish Date: 2024-01-05
  • Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength (RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.
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