期刊文献+

顾及多维信息的改进WKNN指纹匹配算法

An improved WKNN fingerprint matching algorithm considering multidimensional information
在线阅读 下载PDF
导出
摘要 在复杂室内环境下,接收设备的方向和高度是影响信号强度的关键因素之一。文中以智能手机为载体,利用WiFi指纹定位技术,针对指纹特征匹配过程中受外界环境干扰而引起的指纹匹配度差的问题,考虑方向和高度信息对指纹信号的影响,提出一种顾及多维信息的改进WKNN指纹匹配算法。该算法在离线阶段以及在线阶段引入方向和高度信息,分方向和高度采集WiFi、气压计数据和航向角数据,针对不同方向和高度的数据构建不同的指纹数据库。在线阶段根据测试点航向角数据判断当前方向,根据气压计值判断当前高度,选择合适方向和高度的指纹数据库进行匹配定位。实验结果表明,考虑多维信息的平均定位误差从1.5497 m降低到1.3269 m,误差减少14.28%,定位精度明显提高,该方法为指纹定位性能优化提供新思路。 In complex indoor environments,the direction and height of the receiving device are one of the key factors affecting signal strength.This paper uses smartphones as the carriers and utilizes WiFi fingerprint positioning technology to address the problem of poor fingerprint matching caused by external environmental interference during the fingerprint feature matching process.Considering the impact of direction and height information on fingerprint signals,an improved WKNN fingerprint matching algorithm that takes into account multidimensional information is proposed.The method introduces direction and height information in the offline phase and the online phase,collects WiFi,barometer data and heading angle data by direction and altitude,and constructs different fingerprint databases for data of different directions and altitudes.In the online phase,the current direction is judged according to the heading angle data of the test point,and the current height is judged according to the barometer value,and the fingerprint database of the appropriate direction and height is selected for matching and localization.The experimental results show that the average positioning error considering multidimensional information has been reduced from 1.5497 m to 1.3269 m,with an error reduction of 14.28%,and the positioning accuracy has been significantly improved,and this method provides a new idea to improve the fingerprint localization performance.
作者 张帅 汪云甲 王威 ZHANG Shuai;WANG Yunjia;WANG Wei(School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China;School of Smart Cities,Chongqing Jiaotong University,Chongqing 400074,China;Zhejiang Deqing Zhilu Navigation Research Institute Co.,Ltd.,Deqing,Zhejiang,3132006,China;Jiangsu Power Design Institute Co,.Ltd.of China Energy Engineering Group,Nanjing 210000,China)
出处 《测绘工程》 2025年第6期1-7,17,共8页 Engineering of Surveying and Mapping
基金 重庆市教委科学技术研究项目(KJQN202400719)。
关键词 位置指纹 高度信息 方向信息 fingerprint height information direction information
  • 相关文献

参考文献12

二级参考文献103

  • 1孙伟,李亚丹,黄恒,杨一涵.基于级联滤波的建筑结构信息/惯导室内定位方法[J].仪器仪表学报,2021,42(3):10-16. 被引量:13
  • 2徐爱功,杜健,隋心,史政旭,房穹.零速修正与完备监测在UWB/MEMS IMU组合中的应用[J].测绘科学,2022,47(12):1-7. 被引量:5
  • 3李瑛,胡志刚,刘洋.一种基于BP神经网络的室内定位模型[J].计算机应用,2007,27(B06):56-57. 被引量:5
  • 4张明华,张申生,曹健.无线局域网中基于信号强度的室内定位[J].计算机科学,2007,34(6):68-71. 被引量:66
  • 5Pahlavan K, Li Xinrong. Indoor Geo-location Sci-ence and Technology [ J ]. IEEE CommunicationsMagazine,2002,40(2) : 112-118.
  • 6Jaegeol Yim. Introducing a Decision Tree-based In-door Positioning Technique [J]. Expert Systems-with Applications , 2008,34(2) : 1 296-1 302.
  • 7Ling Pei, Chen Ruizhi, Liu Jingbin. Using Inquiry-based Bluetooth RSSI Probability Distributions forIndoor Positioning[J], Journal of Global Positio-ning Systems , 2010 .9(2) : 122-130.
  • 8Ling Pei, Chen Ruizhi, Liu Jingbin. Inquiry-basedBluetooth Indoor Positioning via RSSI ProbabilityDistributions[C]. 2010 Second International Con-ference on Advances in Satellite and Space Commu-nications, TBD,Athens, Greece,2010.
  • 9Honkavirta V,PeralaT, Ali-Loytty S. A Compara-tive Survey of WLAN Location FingerprintingMethods[C]. Positioning, Navigation and Commu-nication, Hannover, Germany 2009.
  • 10Yousse F,Agrawala M A, Shankar A, et al. AProbabilistic Clustering-Based Indoor Location De-termination System[R]. Technical Reports of theComputer Science Department, Washington D C,2002.

共引文献276

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部