摘要翻译：
在本文中，我们提出了一个精确的可见光室内定位系统的智能手机使用现有的商业发光二极管(LED)灯具。该技术被称为几何光学定位算法(GOPA),它在智能手机上使用空间色码界标和基于到达角(AOA)的几何算法来定位设备，并保留LED的时频域调制，以提高来自相同发光器件基础设施的可见光网络广播的吞吐量。GOPA算法是基于灵活的手势和握手等实际考虑而开发的，它既具有定位鲁棒性，又能在设备上处理。通过引入虚拟平面的思想，在GOPA中解决了基于AOA的定位系统的视场(FOV)问题。该智能手机的接收端采用嵌入式加速度计和前置摄像头来测量智能手机的倾斜度并获取图像。实验结果表明，二维($2$-D)和三维($3$-D)定位是鲁棒的。在忽略倾斜的情况下，2$-D定位的实验平均定位误差为0.54$cm。3$-D定位的实验平均误差分别为1.24$cm、1.85$cm和6.02$cm,在理想的非倾斜和非定向、非倾斜但定向以及倾斜和定向两种情况下，误差分别为1.24$cm、1.85$cm和6.02$cm。
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英文标题：
《GOPA: Geometrical Optics Positioning Algorithm Using Spatial Color Coded
  LEDs (Extended Version)》
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作者：
Hamid Hosseinianfar, Ata Chizari, Jawad A. Salehi
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In this paper, we propose an accurate visible light indoor localization system for a smartphone using the existing commercial light-emitting diode (LED) luminaries. The proposed technique, called geometrical optics positioning algorithm (GOPA), uses spatial color code landmarks alongside angle of arrival (AOA)-based geometrical algorithm on smartphones to locate the device, and reserves LED's time-frequency domain modulation to increase the throughput of the visible light network broadcast from the same luminaries infrastructure. GOPA algorithm is developed with practical considerations such as flexible hand gesture and handshake, and it enables both positioning robustness and on-device processing. By introducing the idea of virtual plane, field of view (FOV) issue of AOA-based positioning systems is addressed in GOPA. The embedded accelerometer and front-facing camera of the smartphone are used at the receiver side to measure the smartphone inclination and acquire the image. Experimental results show robust two-dimensional ($2$-D) and three-dimensional ($3$-D) positioning. The experimental mean positioning error for $2$-D positioning is $0.54$ cm, in case one ignoring the tilt. The experimental mean positioning errors for $3$-D positioning are respectively $1.24$ cm, $1.85$ cm, and $6.02$ cm for ideal non-tilted and non-oriented, non-tilted but orientated, and both tilted and orientated scenarios.
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PDF链接：
https://arxiv.org/pdf/1807.06931