基于多分布密度位置指纹的高效室内定位算法研究

乐燕芬; 汤卓; 盛存宝; 施伟斌

doi:10.11959/j.issn.1000-436x.2019001

您当前的位置：

首页 >

文章列表页 >

基于多分布密度位置指纹的高效室内定位算法研究

学术通信 | 更新时间：2024-06-05

- 基于多分布密度位置指纹的高效室内定位算法研究
- Fast and resource efficient method for indoor localization based on fingerprint with varied scales
- 通信学报 2019年40卷第1期页码：172-179
- 作者机构：
  
  上海理工大学光电信息与计算机工程学院，上海 200093
- 作者简介：
  
  [ "乐燕芬（1978- ），女，浙江宁波人，博士，上海理工大学讲师，主要研究方向为无线传感器网络抗干扰及应用。" ]
  [ "汤卓（1994- ），女，湖南张家界人，上海理工大学硕士生，主要研究方向为无线传感器网络定位技术。" ]
  [ "盛存宝（1992- ），男，内蒙古赤峰人，上海理工大学硕士生，主要研究方向为无线传感器网络定位、无人机控制技术。" ]
  [ "施伟斌（1967- ），男，上海人，博士，上海理工大学副教授，主要研究方向为无线传感器网络协议及应用。" ]
- 基金信息：
  
  上海市重点科技攻关基金资助项目(14511107902)
- DOI：10.11959/j.issn.1000-436x.2019001
  中图分类号： TN92
- 网络出版日期：2019-01，
  
  纸质出版日期：2019-01-25
- 稿件说明：
移动端阅览
乐燕芬, 汤卓, 盛存宝, 等. 基于多分布密度位置指纹的高效室内定位算法研究[J]. 通信学报, 2019,40(1):172-179.

Yanfen LE, Zhuo TANG, Cunbao SHENG, et al. Fast and resource efficient method for indoor localization based on fingerprint with varied scales[J]. Journal on communications, 2019, 40(1): 172-179.
乐燕芬, 汤卓, 盛存宝, 等. 基于多分布密度位置指纹的高效室内定位算法研究[J]. 通信学报, 2019,40(1):172-179. DOI： 10.11959/j.issn.1000-436x.2019001.

Yanfen LE, Zhuo TANG, Cunbao SHENG, et al. Fast and resource efficient method for indoor localization based on fingerprint with varied scales[J]. Journal on communications, 2019, 40(1): 172-179. DOI： 10.11959/j.issn.1000-436x.2019001.

摘要

为提高定位效率，提出了一种基于多分布密度位置指纹、精度渐进的室内定位算法。该算法把定位区域分为多个局部区域，并设定不同分布密度的参考位置点，根据来自锚节点的接收信号强度（RSS）时间和强度分布，通过各局部区域对应的信号覆盖向量和主成分分析法（PCA）提取的稀疏指纹的特征实现层次化匹配，有效减少在线指纹匹配过程的计算量，有利于目标节点存储空间和能耗的优化。实验结果表明，提出的算法在定位精度上不逊于其他室内定位算法，并且对锚节点分布密度依赖度小。

Abstract

To improve the prediction speed in indoor localization

a novel algorithm based on fingerprint with varied scales was proposed.It divided the region of interest into distinct zones with distinctive coverage indicators

and reference positions with different distribution density were set in the region.According the time relevance and strength vary of the RSS from the anchors

the grids-matching process was greatly sped up for the usage of coverage indictors and the features of the location fingerprint extracted with the PCA

which made the proposed method fit the demand of application with limited power and memory.Experimental results indicate that accuracy of the positioning is ensured with the reduced energy-consuming

and more flexible about the number of anchors and the grid distribution.

关键词

Keywords

references

ALI K , GATSIS N , AKOPIAN D . Modern WLAN fingerprinting indoor positioning methods and deployment challenges [J ] . IEEE Communications Surveys ＆ Tutorials , 2017 , 19 ( 3 ): 1974 - 2002 .

ZHENG W , FU K , JEDARI E , et al . A fast and resource efficient method for indoor positioning using received signal strength [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 12 ): 9747 - 9758 .

HUANG C C , MANH H N . RSS-based indoor positioning based on multi-dimensional kernal modeling and weighted average tracking [J ] . IEEE Sensors Journal , 2016 , 16 ( 9 ): 3231 - 3245 .

CHEN Y Q , YANG Q , YIN J , et al . Power-efficient access-point selection for indoor location estimation [J ] . IEEE Transactions on Knowledge and Data Engineering , 2006 , 18 ( 7 ): 877 - 888 .

BAHL P , PADMANABHAN V N . Radar:an in-building RF-based user location and tracking system [C ] // The 19th Annual Joint Conference of IEEE Computer and Communications Societies . 2000 : 775 - 784 .

SANDY M , FARAH M C , PAUL H , et al . Target tracking using machine learning and Kalman filter in wireless sensor networks [J ] . IEEE Sensors Journal , 2014 , 14 ( 10 ): 3715 - 3725 .

SANDY M , FARAH M C , PAUL H , et al . Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks [J ] . IEEE Sensors Journal , 2016 , 16 ( 7 ): 2115 - 2126 .

李华亮 , 钱志鸿 , 田洪亮 . 基于核函数特征提取的室内定位算法研究 [J ] . 通信学报 , 2017 , 38 ( 1 ): 158 - 167 .

LI H L , QIAN Z H , TIAN H L . Research on indoor localization algorithm based on kernel principal component analysis [J ] . Journal on Communications , 2017 , 38 ( 1 ): 158 - 167 .

ZOU H , LUO Y W , LU X X , et al . A mutual information based online access point selection strategy for Wi-Fi indoor localization [C ] // IEEE International Conference on Automation Science ＆ Engineering . 2015 : 180 - 185 .

朱琼琼 , 李平 , 杨程 , 等 . 自适应AP选择无线室内定位算法 [J ] . 计算机工程与应用 , 2018 , 54 ( 14 ): 120 - 126 ,137.

ZHU Q Q , LI P , YANG C , et al . Wireless indoor localization algorithm based on adaptive selection of access point [J ] . Computer Engineering and Applications , 2018 , 54 ( 14 ): 120 - 126 ,137.

AZADEH K , KONSTANTINOS N P , ANASTASIOS N V . Kernel-based positioning in wireless local area networks [J ] . IEEE Transactions on Mobile Computing , 2007 , 6 ( 6 ): 689 - 705 .

ZHAO Q Y , ZHANG S , LIU X C , et al . An effective preprocessing scheme for WLAN-based fingerprint positioning systems [C ] // IEEE 12th International Conference on Communication Technology . 2010 : 592 - 595 .

赵方 , 罗海勇 , 林权 , 等 . 基于核函数法及马尔可夫链的节点定位算法 [J ] . 通信学报 , 2010 , 31 ( 11 ): 195 - 204 .

ZHAO F , LUO H Y , LIN Q , et al . Node localization algorithm based on kernel function and Markov chains [J ] . Journal on Communications , 2010 , 31 ( 11 ): 195 - 204 .

XU Y B , ZHOU M , MA L . Wi-Fi indoor location determination via ANFIS with PCA methods [C ] // IEEE International Conference on Network Infrastructure and Digital Content . 2009 : 647 - 651 .

FANG S H , LIN T N . Principal component localization in indoor WLAN environments [J ] . IEEE Transactions on Mobile Computing , 2012 , 11 ( 1 ): 100 - 109 .

HENDRIK L , THOMAS K.WOLFGANG E.Pre-processing of fingerprints to improve the positioning accuracy of 802 . 11-based positioning system [C ] // The 1st ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environment . 2008 : 73 - 78 .

浏览量

1035

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于核函数特征提取的室内定位算法研究

RFreeTrack：基于差分相位全息图的RFID非接触式鲁棒定位系统

基于分布式MIMO网络的室内终端单站定位技术

基于扩散模型的室内定位射频指纹数据增强方法

基于域自适应的Wi-Fi指纹设备无关室内定位模型