Centimeter-Level 3-D Mobile Online Visible Light Positioning System With Single LED Lamp
Centimeter-Level 3-D Mobile Online Visible Light Positioning System With Single LED Lamp
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DOI：
10.1109/jiot.2023.3285556
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发表时间：
2024-01
影响因子：
10.6
通讯作者：
Shuai Ma;Bing Li;Guanjie Zhang;Hang Li;C. Qiu;Chuang Yu;Shiyin Li;Chao Shen
中科院分区：
文献类型：
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作者：
Shuai Ma;Bing Li;Guanjie Zhang;Hang Li;C. Qiu;Chuang Yu;Shiyin Li;Chao Shen
在本文中，我们考虑了一个实用的室内三维移动在线可见光定位系统，其中用户设备(UE)的定位是任意的。基于多个光电探测器(PD)的接收信号强度(RSS)，我们将三维VLP问题描述为一个非线性最小二乘(NLSS)优化问题，并提出了一种序列二次规划(SQP)定位算法来有效地计算UE的位置。为了获得更准确的定位解，我们进一步利用深度学习的优势，提出了一种基于随机梯度下降(SGD)的VLP算法，平均定位误差为1.77 cm，明显优于现有的RSS VLP定位方法。在此基础上，利用便携式RaspberryPi4Model B作为定位信号处理器和数据存储器，设计了一个三维移动在线VLP系统原型，建立了第一个公开可用的三维VLP测量数据集，包括RSS和定位。通过所设计的原型系统对所提出的定位方案进行了实现和评估，该系统可以达到厘米级的定位精度(在一定条件下小于1厘米)。
In this article, we consider a practical indoor 3-D mobile online visible light positioning (VLP) system, where the orientation of the user equipment (UE) is arbitrary. Based on the received signal strength (RSS) of multiple photodetectors (PDs), we formulate the 3-D VLP problem as a nonlinear least squares (NLSs) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE’s location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD)-based VLP algorithm, and achieve an average positioning error of 1.77 cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3-D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3-D VLP measured data set, including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).