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

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外文摘要：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 posi-tioning systems struggle to overcome the interferences of background and indoor-reflected noise.Meanwhile,when en-suring 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 net-work.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 loca-tion 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 algo-rithm 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.

外文关键词：

Visible-light communicationIndoor positioningFrequency estimationPisarenko harmonic de-compositionBack propagation neural network

作者：

Li ZHAO、Yi REN、Qi WANG、Lange DENG、Feng ZHANG

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作者单位：

School of Electronic Information Engineering,Xi'an Technological University,Xi'an 710021,China

基金：

National Defense Foundation of ChinaGeneral project of industrial field of Shaanxi science and Technology DepartmentXi'an Science and technology project

项目编号：

616713622022GY-0722020KJRC0040

出版年：

2024

DOI：

10.23919/cje.2022.00.161

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(1)

参考文献量31