针对基于接收信号强度的可见光通信系统室内定位精度低的问题, 提出一种基于深度神经网络的可见光通信系统室内定位方法。方法采用可见光信道估计技术进行室内距离测量, 以解决接收信号强度稳定性与可靠性不足的问题。此外, 设计了深度神经网络在离线阶段学习光电二极管距离向量的分布特性, 以避免光信号不稳定导致误差升高的问题.在线上阶段基于多距离向量对目标进行定位, 可在满足时间效率要求的情况下提高定位精度。仿真结果表明, 在室内场景下, 该方法的平均定位精度优于传统三角定位法与基于接收信号强度的定位方法。

Aiming at the problem that the positioning precision of received signal strength based indoors positioning methods for visible light communication system is low, a new indoors positioning method for visible light communication system based on deep neural networks is proposed. In this method, the visible light channel estimation technique is adopted to measure the indoors distance, so that the problems of insufficient stability and reliability of the received signal strength are resolved. Besides, a deep neural network is designed to learn the distribution characteristics of the distance vectors of the photodiode in offline phase, in order to avoid the problem that the instable light signals lead to error growth. In online phase, the target is positioned based on multiple distance vectors, thus the positioning precision can be improved further, at the same time, the time efficiency meets the requirements. Simulation results show that, in the indoors scenario, the proposed method achieves better positioning precision than traditional triangulation methods and received signal strength based positioning methods.