Laser & Optoelectronics Progress, Volume. 58, Issue 17, 1706008(2021)
Indoor Visible Light Fingerprint Positioning Scheme Based on Convolution Neural Network
This paper proposes a visible light fingerprint positioning scheme based on a convolutional neural network (CNN) to improve the performance of indoor visible light positioning systems. In the proposed scheme, optical intensity signals are employed as the features of the reference node LED, and receiver coordinates are employed as training labels to construct fingerprint database. In addition, a positioning model based on light intensity information is constructed, and a one-dimensional CNN learning model is adopted for training. CNN application solves the problems of low-positioning accuracy and poor stability of the fully-connected feedforward neural network method. In an indoor-positioning scene (size: 5 m×5 m×3 m), the proposed positioning scheme obtained high positioning accuracy with an average positioning error of 4.44 cm. In addition, the performance of several different indoor visible light positioning methods was compared and analyzed in simulation experiments, and the results verified the technical advantages of the proposed scheme.
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Hao Xu, Xudong Wang, Nan Wu. Indoor Visible Light Fingerprint Positioning Scheme Based on Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(17): 1706008
Category: Fiber Optics and Optical Communications
Received: Nov. 18, 2020
Accepted: Dec. 14, 2020
Published Online: Sep. 14, 2021
The Author Email: Wang Xudong (wxd@dlmu.edu.cn)