成像型可见光定位系统优化研究

刘翔宇; 刘保睿; 宋嵩; 郭磊

doi:10.11999/JEIT211019

成像型可见光定位系统优化研究

doi: 10.11999/JEIT211019

刘翔宇¹,
刘保睿²,
宋嵩^2, ,,
郭磊^{1, 3}

1.
重庆邮电大学通信与信息工程学院，智能通信与网络安全研究院重庆 400065
2.
东北大学计算机科学与工程学院沈阳 110819
3.
杭州中科先进技术研究院杭州 310005

基金项目: 重庆市高校创新研究群体(CXQT21019)

详细信息

作者简介:
刘翔宇：男，博士后，研究方向为可见光定位和无线光通信

刘保睿：男，硕士生，研究方向为可见光通信和可见光定位

宋嵩：男，博士生，研究方向为自由空间光通信和无线光通信

郭磊：男，教授，杭州市钱江特聘专家，研究方向为通信网络技术

通讯作者:
宋嵩　songsong@stumail.neu.edu.cn

¹⁾单位bit宽度指1位编码形成的条纹在图像中所占像素点的个数。
²⁾黑2表示该条纹是最短黑条纹宽度的2倍。
中图分类号: TN92
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- HTML全文浏览量: 318
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-24
- 修回日期: 2021-12-03
- 录用日期: 2021-12-08
- 网络出版日期: 2021-12-18
- 刊出日期: 2022-12-16

Research on Optimization of Camera-based Visible Light Positioning System

LIU Xiangyu¹,
LIU Baorui²,
SONG Song^{2
, ,},
GUO Lei^{1, 3}

1.
Institute of Intelligent Communication and Network Security, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
3.
Hangzhou Institute of Advanced Technology, Hangzhou 310005, China

Funds: Chongqing University Innovation Research Group (CXQT21019)

摘要

摘要: 现有室内成像型可见光定位系统多注重定位精度的提高，而忽略了系统的解码识别成功率和定位算法的适用性。表现在：(1)接收端捕获带有模糊效应的图像会导致解码识别率变低；(2)定位系统移动状态下采用单一算法定位，且双灯(dual-LED)算法的旋转角存在非确定性，导致定位精度大幅度变化，甚至定位系统失效。该文首先提出基于条纹宽度比的解码算法，消除人为设定硬阈值的缺陷；然后，提出基于旋转角优化的联合定位算法，利用方向传感器辅助确认旋转角并联合定位；此外，还设计了简易导航功能。实验结果表明，该算法使得解码识别率在1.5 m内可达99%，系统平均定位误差为3.998 cm。
- 可见光定位 /
- 条纹宽度比 /
- 定位算法 /
- 优化系统
Abstract: The existing indoor visible light positioning systems focus more on the positioning accuracy improvement and ignores the system's decoding success rate and the applicability of the positioning algorithm. The specific aspects are as follows: (1) Captured images with the blurring effect result in a lower decoding recognition rate; (2) Most existing systems use a single algorithm to achieve the positioning, and the dual-Light Emitting Diode (dual-LED) positioning algorithm exists the uncertainty for the rotation angle. The above problems result in the positioning accuracy decreasing or even the positioning system failure. First, the decoding algorithm based on the fringe width ratio is proposed to remove the defect of the artificial hard-threshold. Then, the joint positioning algorithm based on optimized rotation angle is proposed, which uses the smartphone orientation sensor to calibrate the rotation angle. Furthermore, a simple navigation application is designed. Experimental results show that the designed algorithms raise the decoding recognition rate up to 99% within 1.5 m, and the average positioning error is 3.998 cm.
- Visible light positioning /
- Fringe width ratio /
- Positioning algorithm /
- Optimization system
¹⁾单位bit宽度指1位编码形成的条纹在图像中所占像素点的个数。

²⁾黑2表示该条纹是最短黑条纹宽度的2倍。

HTML全文

图 1 室内可见光定位技术应用场景

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图 2 基于图像处理的室内可见光定位系统

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图 3 捕获LED图像二值化后进行解码

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图 4 两盏LED的可见光定位算法

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图 5 1盏LED 的可见光定位算法

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图 6 子图中包含序列头的不同情形

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图 7 条纹种类示意

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图 8 基于旋转角优化的联合定位算法

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图 9 导航算法模拟场景及实施方案

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图 10 实验测试场景实物图

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图 11 发送端与接收端的垂直距离对解码识别率的影响

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图 12 接收端移动状态下定位结果分布

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图 13 接收端定点状态下的部分定位结果分布

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图 14 误差累积分布函数

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图 15 导航效果展示

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表 1 系统参数设置

参数	值
实验区域大小	3.2 m×3.0 m×2.65 m
LED型号	CXA1512
LED最大功率	21.6 W
灯罩直径	20 cm
芯片型号	Cylone IV EP4CE6F
放大电路	LM324N
MOS管型号	IRF520
电源	GPS-430C
智能手机型号	Google Pixel
屏幕分辨率	1920×1080
系统版本	Android 7.0/8.0
前置摄像头分辨率	2448×3264
相机曝光时间	100 µs
相机感光度	64

下载: 导出CSV

表 2 信标编码参数

参数	值(µs)
序列头宽度	400
序列尾宽度	80
时隙（单位编码）宽度	80

下载: 导出CSV

参考文献(15)

[1]	WU Xiping, SOLTANI M D, ZHOU Lai, et al. Hybrid LiFi and WiFi networks: A survey[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 1398–1420. doi: 10.1109/COMST.2021.3058296
[2]	LIU Fen, LIU Jing, YIN Yuqing, et al. Survey on WiFi-based indoor positioning techniques[J]. IET Communications, 2020, 14(9): 1372–1383. doi: 10.1049/iet-com.2019.1059
[3]	BERNARDINI F, BUFFI A, FONTANELLI D, et al. Robot-based indoor positioning of UHF-RFID tags: The SAR method with multiple trajectories[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 8001415. doi: 10.1109/TIM.2020.3033728
[4]	SPACHOS P and PLATANIOTIS K N. BLE beacons for indoor positioning at an interactive IoT-based smart museum[J]. IEEE Systems Journal, 2020, 14(3): 3483–3493. doi: 10.1109/JSYST.2020.2969088
[5]	YAO C Y and HSIA W C. An indoor positioning system based on the dual-channel passive RFID technology[J]. IEEE Sensors Journal, 2018, 18(11): 4654–4663. doi: 10.1109/JSEN.2018.2828044
[6]	FENG Daquan, WANG Chunqi, HE Chunlong, et al. Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation[J]. IEEE Internet of Things Journal, 2020, 7(4): 3133–3146. doi: 10.1109/JIOT.2020.2965115
[7]	ZHOU Mu, LI Xinyue, WANG Ya, et al. 6G multisource-information-fusion-based indoor positioning via Gaussian kernel density estimation[J]. IEEE Internet of Things Journal, 2021, 8(20): 15117–15125. doi: 10.1109/JIOT.2020.3031639
[8]	KOMINE T and NAKAGAWA M. Fundamental analysis for visible-light communication system using LED lights[J]. IEEE transactions on Consumer Electronics, 2004, 50(1): 100–107. doi: 10.1109/TCE.2004.1277847
[9]	LUO Junhai, FAN Liying, and LI Husheng. Indoor positioning systems based on visible light communication: State of the art[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2871–2893. doi: 10.1109/COMST.2017.2743228
[10]	GU Wenjun, AMINIKASHANI M, DENG Peng, et al. Impact of multipath reflections on the performance of indoor visible light positioning systems[J]. Journal of Lightwave Technology, 2016, 34(10): 2578–2587. doi: 10.1109/JLT.2016.2541659
[11]	KUO Yesheng, PANNUTO P, HSIAO K J, et al. Luxapose: Indoor positioning with mobile phones and visible light[C]. Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, USA, 2014: 447–458.
[12]	LIU Xiangyu, WEI Xuetao, and GUO Lei. DIMLOC: Enabling high-precision visible light localization under dimmable LEDs in smart buildings[J]. IEEE Internet of Things Journal, 2019, 6(2): 3912–3924. doi: 10.1109/JIOT.2019.2893251
[13]	WANG Zeyu, YANG Zhice, HUANG Qianyi, et al. ALS-P: Light weight visible light positioning via ambient light sensor[C]. Proceedings of 2019 IEEE Conference on Computer Communications, Paris, France, 2019: 1306–1314.
[14]	ZHANG Chi and ZHANG Xinyu. Pulsar: Towards ubiquitous visible light localization[C]. Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, Snowbird, USA, 2017: 208–221.
[15]	ZHANG Chi, TABOR J, ZHANG Jialiang, et al. Extending mobile interaction through near-field visible light sensing[C]. The 21st Annual International Conference on Mobile Computing and Networking, Paris, France, 2015: 345–357.