基于注意力机制的可见光定位单元模型复制  

Attention Mechanism for Visible Light Positioning Unit Model Replication

作　　者：王创世 陈勇[1] 刘焕淋[2] 吴金兰 陈豪[1] 张薇薇 Wang Chuangshi;Chen Yong;Liu Huanlin;Wu Jinlan;Chen Hao;Zhang Weiwei(Key Laboratory of Industrial Internet of Things&Network Control,Ministry of Education,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学工业物联网与网络化控制教育部重点实验室,重庆400065 [2]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《中国激光》2024年第8期156-165,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(51977021);重庆市自然科学基金(CSTB2023NSCQ-MSX0734);重庆市研究生科研创新项目(CYS22483)。

摘　　要：针对室内可见光定位接收信号强度易出现波动从而产生较大定位误差,以及从一个定位单元迁移到其他定位单元可能会降低定位精度的问题,笔者提出了一种基于注意力机制的卷积神经网络的室内三维定位方法,以减小接收信号波动产生的影响,并采用迁移学习将在第一个定位单元中训练的网络迁移到其他定位单元中,在保证定位精度不变的前提下减少了训练网络的成本。仿真结果表明:所提算法在5 m×5 m×3 m的定位单元内可以实现平均误差为3.54 cm的三维定位;采用迁移学习将已训练网络部署到第二个定位单元中,可以实现平均误差为3.67 cm的定位。实验结果表明:在1.2 m×0.75 m×1.2 m的定位单元实验中,所提算法可以实现平均误差为3.32 cm的三维定位,90%的误差分布在4.12 cm内;采用迁移学习将已训练网络部署到第二个定位单元中,可以实现平均误差为3.35 cm的定位。与现有算法相比,所提算法迁移前后的定位精度均有所改善。Objective With the rapid development and application of the Internet of Things(IoT)and indoor activities,high-precision indoor positioning technology based on location services has a wide range of applications.Given that GPS and Beidou signals lead to signal attenuation when penetrating buildings,it is impossible to realize accurate indoor positioning,and an effective indoor positioning method is urgently required to compensate for the vacancy of high-precision indoor positioning.Compared with other indoor positioning methods,visible light indoor positioning,based on the received signal strength,can be used as an effective indoor positioning method owing to its advantages of low cost,high precision,and ease of deployment.However,the existence of multipath effect,shadow change,receiver thermal effect,and other problems can lead to fluctuations in the strength of the received indoor visible light positioning signal,and thereby,resulting in large positioning errors.Furthermore,in extant studies,researchers tend to solely examine one positioning unit and assume that it can be completely copied to other positioning units.However,the migration from one positioning unit to other positioning units may lead to high positioning errors due to different LED positions,varying noise levels,and other differences.Therefore,it is important to solve the jitter problem of the received signal and improve the accuracy of indoor positioning.Methods To address the problem of jitter in received signals,in this study,a convolutional neural network was proposed based on an attention mechanism(Fig.4)to reduce the impact of fluctuations in received signals.First,a fast Fourier transform was used to preprocess the received time-domain signal strength values,and the power spectra of the signals were obtained.A CNN with an attention mechanism was used to extract the features of the signal power spectrum,and a channel attention module(Fig.5)was used to increase the weights for each channel to reduce the influence of redundant information on the

关 键 词：光通信 可见光定位 注意力机制 卷积神经网络 迁移学习 

分 类 号：O436[机械工程—光学工程]