基于双BP神经网络的室内可见光定位算法  

Indoor visible light positioning algorithm based on double BP neural network

作　　者：秦岭 刘哲 王凤英[1] 史明泉[1] 胡晓莉[1] QIN Ling;LIU Zhe;WANG Fengying;SHI Mingquan;HU Xiaoli(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou Inner Mongolia 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《光通信技术》2021年第2期1-5,共5页Optical Communication Technology

基　　金：国家自然科学基金(61961033)资助;内蒙古自然科学基金(2019MS06021、2019LH06005)资助;内蒙古自治区高等学校“青年科技英才支持计划”(NJYT-19-A15)资助。

摘　　要：为提高室内可见光定位的精度,提出了一种基于双反向传播(BP)神经网络的单发光二极管(LED)灯室内定位算法。首先在定位区域内使用BP神经网络确定待测目标粗略的位置范围,然后以该位置范围为限制条件,再次使用BP神经网络实现更精确的定位。室内定位系统采用单个LED灯作为发射器,3个水平光电探测器作为接收器接收光功率,避免了使用多个LED灯在定位时引起的光源符号间干扰。仿真结果表明:在3 m×3 m×3.5 m的定位区域内,提出算法的平均定位精度可达0.0042 m,比传统的室内可见光定位算法高。In order to improve the accuracy of indoor visible light positioning,a single light-emitting diode(LED)lamp indoor po-sitioning algorithm based on double back propagation(BP)neural network is proposed.The algorithm first uses the BP neural net-work to determine the rough position range of the target in the positioning area,and then uses the position range as a limiting condition to use the BP neural network again to achieve more accurate positioning.The indoor positioning system uses a single LED lamp as the transmitter and three horizontal photodetectors as the receiver to receive optical power,which avoids the inter-symbol interference caused by using multiple LED lamps during positioning.The simulation results show that in the positioning area of 3 m×3 m×3.5 m,the average positioning accuracy of the algorithm in the paper can reach 0.0042 m,which is higher than the traditional indoor visible light positioning algorithm.

关 键 词：可见光 接收光功率 室内定位 反向传播神经网络 

分 类 号：TN929.12[电子电信—通信与信息系统]