基于可见光通信的BP神经网络室内定位算法

BP Neural Network Indoor Localization Algorithm Based on Visible Light Communication

提出一种基于可见光通信的BP神经网络室内定位算法,首先通过MDS-MAP算法和最小二乘法获得全网节点的相对坐标,再利用信源节点的坐标信息得到网络内所有节点的绝对坐标,最后通过单隐层BP神经网络优化定位结果。仿真结果表明,该算法比MDS-MAP算法和MDS-MAP(P)算法的相对定位误差小,应用于室内定位可以得到更高的定位精度。

In this paper,a BP neural network indoor location algorithm based on visible light communication(VLC)was proposed.Firstly,the relative coordinates of the nodes in the whole network were obtained by both the MDS-MAP algorithm and the least squares algorithm,and then the absolute coordinates of all nodes were obtained from the coordinates of the source nodes.Finally,the positioning result was optimized with the single-hidden layer BP neural network.Simulation results show that compared with MDS-MAP and MDS-MAP(P)algorithms,the proposed algorithm shows smaller relative positioning error,and can obtain higher accuracy in indoor positioning.