基于卷积神经网络的室内可见光指纹定位方法

Indoor Visible Light Fingerprint Positioning Scheme Based on Convolution Neural Network

为进一步提高室内可见光定位系统性能,提出了一种基于卷积神经网络(CNN)的可见光指纹定位方法。该方法利用参考节点LED的光强信号作为特征,构建指纹数据库,将接收器坐标作为训练标签,引入一维CNN学习模型进行训练,建立基于光强信息的定位模型。CNN的应用,较好地解决了全连接前馈神经网络定位精度低、稳定性差的问题。在室内5 m×5 m×3 m的定位场景下,利用所提定位方法可以获得平均定位误差为4.44 cm的定位精度。通过仿真实验,对比分析了不同室内可见光定位方法的性能,验证了所提方法的技术优势。

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.