DS-TWR算法室内定位批量测距系统的优化研究

doi:10.11959/j.issn.1000-0801.2022018

电信科学 ›› 2022, Vol. 38 ›› Issue (1): 73-82.doi: 10.11959/j.issn.1000-0801.2022018

DS-TWR算法室内定位批量测距系统的优化研究

孙宏伟, 曹雪虹, 焦良葆, 孟琳, 刘子恒, 袁枫

南京工程学院人工智能产业技术研究院，江苏南京 211167

修回日期:2022-01-10 出版日期:2022-01-20 发布日期:2022-01-01
作者简介:孙宏伟（1997- ），男，南京工程学院人工智能产业技术研究院硕士生，主要研究方向为室内定位
曹雪虹（1964- ），女，博士，南京工程学院人工智能产业技术研究院教授，主要研究方向为无线通信系统、信息理论
焦良葆（1972- ），男，博士，南京工程学院人工智能产业技术研究院教授，主要研究方向为图像信号处理、视觉信息理解
孟琳（1989- ），女，博士，南京工程学院人工智能产业技术研究院讲师，主要研究方向为控制科学与技术
刘子恒（1997- ），男，南京工程学院人工智能产业技术研究院硕士生，主要研究方向为室内定位
袁枫（1997- ），男，南京工程学院人工智能产业技术研究院硕士生，主要研究方向为室内定位
基金资助:
国家自然科学基金青年基金资助项目(61903183)

Research on optimization of indoor positioning batch ranging system based on DS-TWR algorithm

Hongwei SUN, Xuehong CAO, Liangbao JIAO, Lin MENG, Ziheng LIU, Feng YUAN

Institute of Artificial Intelligence Industry Technology, Nanjing Institute of Technology, Nanjing 211167, China

Revised:2022-01-10 Online:2022-01-20 Published:2022-01-01
Supported by:
The National Nature Science Foundation Youth Fund of China(61903183)

摘要/Abstract

摘要：

摘要：针对现阶段基于双边双向测距（double sided two-way ranging，DS-TWR）算法的超宽带（ultra wide band， UWB）室内定位系统存在通信次数较多、多标签环境下冲突率较高的问题，提出了一种改进的算法。该方法通过Hash算法对标签和基站的通信内容进行哈希分布，使得基站在每次测距流程中，能够对多个标签进行有规则的统一回复，大大减少了基站发送RES（responds）数据的次数。结果表明，改进算法后，单个标签和基站的通信次数较传统DS-TWR算法减少了15%，增加了基站接收状态在测距中的时间占比，由此降低了基站在接收RNG （range）数据包的冲突率，测距成功率提高了 43.6%。由于每个定位周期内所需要通信次数的减少且数据包之间冲突率的降低，将需要更小的信道容量，由此增加了定位系统的标签容纳量，具有较强的工程意义。

关键词: 超宽带定位, 室内定位, 哈希算法, 双边双向测距

Abstract:

Aiming at the current problems of the ultra wide band (UWB) indoor positioning system based on the double sided two-way ranging (DS-TWR) algorithm, there are more communication times and a higher conflict rate in a multi-tag environment, an improved algorithm was proposed.The Hash algorithm to hash and distribute the communication content between the tag and the anchor was used, so that the anchor could reply to multiple tags in a regular and unified manner during each ranging process, which greatly reduced the number of times the anchor sends RES (responds) data.Experimental results show that the improved algorithms’ communication times between a tag and the anchor were reduced by 15% compared with the traditional DS-TWR algorithm, which increases the time proportion of the anchor receiving state in ranging, thereby reducing the anchor receiving RNG (range) data.The collision rate of packets, the success rate of ranging has increased by 43.6%.Due to the reduction in the number of communications required in each positioning cycle and the reduction in the collision rate between data packets, a smaller channel capacity would be required, thereby increasing the label capacity of the positioning system, which has strong engineering significance.

Key words: UWB, indoor positioning, hash algorithm, DS-TWR

中图分类号:

TN925

孙宏伟, 曹雪虹, 焦良葆, 孟琳, 刘子恒, 袁枫. DS-TWR算法室内定位批量测距系统的优化研究[J]. 电信科学, 2022, 38(1): 73-82.

Hongwei SUN, Xuehong CAO, Liangbao JIAO, Lin MENG, Ziheng LIU, Feng YUAN. Research on optimization of indoor positioning batch ranging system based on DS-TWR algorithm[J]. Telecommunications Science, 2022, 38(1): 73-82.

图/表 12

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

图11

参考文献 22

[1]	万明远 . 火力发电厂当前面临的问题及其对策[J]. 电子世界, 2017(11): 61.
	WAN M Y . Current problems and countermeasures of thermal power plants[J]. Electronics World, 2017(11): 61.
[2]	常琳 . UWB技术在精准人员管理系统中的应用[J]. 煤矿安全, 2021,52(5): 163-165,169.
	CHANG L . Application of UWB technology in accurate personnel positioning system[J]. Safety in Coal Mines, 2021,52(5): 163-165,169.
[3]	赵林, 孙增荣, 王纪强 ,等. 基于超宽带技术的矿用人员精准定位系统[J]. 山东科学, 2021,34(2): 48-53.
	ZHAO L , SUN Z R , WANG J Q ,et al. Precise positioning system for mine personnel based on ultra wide band technology[J]. Shandong Science, 2021,34(2): 48-53.
[4]	陶偲 . 基于UWB的室内SDS-TWR测距算法优化和定位算法融合的研究[D]. 武汉:华中师范大学, 2016.
	TAO (C/S) . Research on optimization of SDS-TWR and fusion of location algorithm in indoor position based on UWB[D]. Wuhan:Central China Normal University, 2016.
[5]	刘关迪, 赵璐 . 基于 UWB 技术的室内智能跟随系统的实现与优化[J]. 计算机应用与软件, 2020,37(12): 95-100.
	LIU G D , ZHAO L . Improvement and implementation of indoor intelligent following system based on UWB technology[J]. Computer Applications and Software, 2020,37(12): 95-100.
[6]	叶伟 . 煤矿井下目标定位的研究现状与展望[J]. 中国矿业, 2021,30(1): 82-89,105.
	YE W . Status and prospects of research on positioning of targets in underground coal mines[J]. China Mining Magazine, 2021,30(1): 82-89,105.
[7]	曾纪钧, 沈桂泉, 张金波 . 基于 UWB 定位技术的作业风险管控智能终端设备研究[J]. 电测与仪表, 2021: 1-9.
	ZENG J J , SHEN G Q , ZHANG J B . Research on intelligent terminal equipment for operation risk management and control based on UWB positioning technology[J]. Electrical Measurement and Instrumentation, 2021: 1-9.
[8]	刘琦, 沈锋, 王锐 . 基于UWB定位系统硬件平台设计[J]. 电子科技, 2019,32(10): 22-27.
	LIU Q , SHEN F , WANG R . Design of hardware platform for UWB positioning system[J]. Electronic Science and Technology, 2019,32(10): 22-27.
[9]	朱冰, 陶晓文, 赵健 ,等. 智能汽车两阶段UWB定位算法[J]. 交通运输工程学报, 2021,21(2): 256-266.
	ZHU B , TAO X W , ZHAO J ,et al. Two-stage UWB positioning algorithm of intelligent vehicle[J]. Journal of Traffic and Transportation Engineering, 2021,21(2): 256-266.
[10]	ZHANG W , ZHU X Z , ZHAO Z Q ,et al. High accuracy positioning system based on multistation UWB time-of-flight measurements[C]// Proceedings of 2020 IEEE International Conference on Computational Electromagnetics. Piscataway:IEEE Press, 2020: 268-270.
[11]	许佩佩 . 基于 TOA 方案的超宽带室内定位技术研究[D]. 南京:东南大学, 2016.
	XU P P . Research of UWBindoor positioning technology based on TOA scheme[D]. Nanjing:Southeast University, 2016.
[12]	SANGC L , ADAMSM , H?RMANNT ,et al. Numerical and experimental evaluation of error estimation for two-way ranging methods[J]. Sensors (Basel,Switzerland), 2019,19(3): 616.
[13]	顾慧东, 焦良葆, 周健 . 室内 UWB 通信高精度定位系统设计[J]. 软件导刊, 2020,19(4): 159-163.
	GU H D , JIAO L B , ZHOU J . Indoor high-precision positioning system design based on UWB[J]. Software Guide, 2020,19(4): 159-163.
[14]	袁枫, 焦良葆, 陈楠 ,等. 室内定位中 DS-TWR 测距算法的优化[J]. 计算机与现代化, 2021(10): 100-106.
	YUAN F , JIAO L B , CHEN N ,et al. Optimization of DS-TWR ranging algorithm in indoor positioning[J]. Computer and Modernization, 2021(10): 100-106.
[15]	刘永立, 吴闻轩, 单麒源 ,等. 井下人员定位的超宽带 TWR改进算法[J]. 黑龙江科技大学学报, 2021,31(2): 135-139.
	LIU Y L , WU W X , SHAN Q Y ,et al. Improvement algorithm of ultra wide band TWR for underground personnel positioning[J]. Journal of Heilongjiang University of Science and Technology, 2021,31(2): 135-139.
[16]	ZHANGK , SHENC , GAO Q , . Research on similarity metric distance algorithm for indoor and outdoor firefighting personnel precision wireless location system based on vague set on UWB[C]// Proceedings of 2017 IEEE 17th International Conference on Communication Technology. Piscataway:IEEE Press, 2017: 1162-1165.
[17]	严嘉祺 . 基于 UWB 的室内定位系统的算法与误差分析[D]. 哈尔滨工业大学, 2020.
	YAN J Q . Algorithm and error analysis of indoor positioning system based on UWB[D]. Harbin Institute of Technology, 2020.
[18]	LIU J H , JING H , XIANG Y X ,et al. Simulation Research of UWB location algorithm[C]// Proceedings of 2018 Chinese Control and Decision Conference (CCDC). Piscataway:IEEE Press, 2018: 4825-4830.
[19]	朱自强 . 超宽带定位的关键技术研究[D]. 成都:电子科技大学, 2019.
	ZHU Z Q . Research on the key technology of ultra-wideband positioning[D]. Chengdu:University of Electronic Science and Technology of China, 2019.
[20]	范强, 张涵, 隋心 . UWB TW-TOA 测距误差分析与削弱[J]. 测绘通报, 2017(9): 19-22,50.
	FAN Q , ZHANG H , SUI X . Error analysis and weakening of UWB TW-TOA ranging[J]. Bulletin of Surveying and Mapping, 2017(9): 19-22,50.
[21]	杨庆生, 蔡文郁, 官靖凯 ,等. 基于长距离UWB的无人水面艇定位技术研究[J]. 杭州电子科技大学学报(自然科学版), 2021,41(3): 30-36.
	YANG Q S , CAI W Y , GUAN J K ,et al. Long-distance localization system applied to ocean unmanned surface vessel[J]. Journal of Hangzhou Dianzi University (Natural Sciences), 2021,41(3): 30-36.
[22]	ZHANG X J , LIU Y J , LI S D ,et al. Simulation and analysis of influence of ranging error on location estimation accuracy[C]// Proceedings of 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP). Piscataway:IEEE Press, 2017: 1-3.

标签ID	系统状态	样本数量/个	通信时长/h	有效通信时长/h	距离均值/m	距离标准差
1	改进前	791 711	20	19	4.476 06	2.282 39
1	改进后	668 276	19	19	4.304 11	2.238 83
2	改进前	785 710	22	18	2.274 93	0.993 38
2	改进后	638 276	18	17	2.209 15	0.960 32

DS-TWR算法室内定位批量测距系统的优化研究

Research on optimization of indoor positioning batch ranging system based on DS-TWR algorithm

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 22

相关文章 12

Metrics

推荐阅读 0

[1]	万潇阳, 孙耀华, 王则予. 6G室内定位技术原理与展望[J]. 电信科学, 2021, 37(6): 91-104.
[2]	赵大明,王欣. 基于VLC的室内定位技术研究[J]. 电信科学, 2019, 35(3): 122-126.
[3]	赵红梅,赵杰磊. 超宽带室内定位算法综述[J]. 电信科学, 2018, 34(9): 130-142.
[4]	杨宇,陈明,刘大庆. 基于分布式LTE网络的室内定位系统[J]. 电信科学, 2018, 34(6): 91-97.
[5]	徐小良,高健,黄河,马哲. 基于RSS空间线性相关的WLAN位置指纹定位算法[J]. 电信科学, 2017, 33(3): 14-21.
[6]	吕亚娟,刘珊,胡荣贻,张文浩. 一种基于多天线簇的新型LTE室内定位系统[J]. 电信科学, 2017, 33(3): 67-75.
[7]	朱亚萍,夏玮玮,章跃跃,燕锋,左旭舟,沈连丰. 基于RSSI和惯性导航的融合室内定位算法[J]. 电信科学, 2017, 33(10): 99-106.
[8]	张梦丹,卢光跃,王宏刚,刘继明. 基于线性内插法改进的室内定位算法[J]. 电信科学, 2017, 33(1): 9-15.
[9]	杨明极,刘恺怿,邵丹. 用于WLAN室内定位的PCA聚类算法[J]. 电信科学, 2016, 32(7): 21-26.
[10]	张梦丹,卢光跃,王宏刚,刘继明. 基于指纹算法的无线室内定位技术[J]. 电信科学, 2016, 32(10): 77-86.
[11]	刘畅,张迅. WLAN接入点的室内定位技术[J]. 电信科学, 2015, 31(2): 135-139.
[12]	于秦,肖志辉,姜微. 基于WLAN和WSN的异构网络协同定位[J]. 电信科学, 2013, 29(7): 83-88.