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基于副载波调制的室内可见光定位系统研究 被引量:1

Research of indoor visible light positioning system based on sub-carrier modulation
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摘要 在室内可见光定位技术的实际应用中,由于环境光的存在使得接收信噪比降低,导致基于接收信号强度的可见光定位技术面临挑战。针对这一问题提出一种基于副载波调制的可见光三维定位系统,利用在接收端对副载波的频率选择来降低环境光对接收信号强度的干扰,通过非相干解调得到LED的身份信息和接收信号强度,并采用骨干粒子群优化算法获得定位目标的位置估计。根据上述方法设计了接收端和发送端,并在1 m×1 m×1.89 m的空间中分别进行了不同高度和不同强度环境光下的三维定位测试。结果表明,所有测试点的三维定位平均误差约为3.00 cm;在高度为0.21 m环境光为0、10和30 lx时平均定位误差分别为2.83、2.98和2.83 cm。证明了基于副载波调制的可见光定位系统可以有效降低由环境光引起的定位误差。 In the practical application of indoor visible light positioning technology,the signal-to-noise ratio is reduced to some extent because of the effect of the ambient light.This is a challenge to the visible light positioning system based on the received signal strength.A visible three-dimensional positioning system based on sub-carrier modulation is studied.The interference of the ambient light on signals is decreased using sub-carrier frequency selection in the receiver.The identity information and received signal strength of LED is obtained by noncoherent demodulation.The target′s location is computed based on an bare-bones particle swarm optimization algorithm.The three-dimensional positioning tests under different intensities of ambient light and different heights are carried out in a space of 1 m×1 m×1.89 m.The results show that the average three-dimensional positioning error of all test points is around 3.00 cm,and the positioning errors are 2.83,2.98 and 2.83 cm respectively when the height is 0.21 m and the illuminations of ambient light are 0,10 and 30 lx.Experiment shows that the visible light positioning system based on subcarrier modulation can effectively reduce the positioning error caused by ambient light.
作者 刘健 蔡喜平 程贵东 胡延静 卢钰松 Liu Jian;Cai Xiping;Cheng Guidong;Hu Yanjing;Lu Yusong(School of Physical Science and Technology,Heilongjiang University,Harbin 150080,China)
出处 《电子测量技术》 北大核心 2022年第1期15-21,共7页 Electronic Measurement Technology
基金 黑龙江大学研究生创新科研项目(YJSCX2021-084HLJU)资助。
关键词 可见光定位 副载波调制 骨干粒子群优化 visible light positioning sub-carrier modulation bare-bones particle swarm optimization
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