基于核函数特征提取的室内定位算法研究

李华亮; 钱志鸿; 田洪亮

doi:10.11959/j.issn.1000-436x.2017018

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基于核函数特征提取的室内定位算法研究

学术通信 | 更新时间：2024-06-05

- 基于核函数特征提取的室内定位算法研究
- Research on indoor localization algorithm based on kernel principal component analysis
- 通信学报 2017年38卷第1期页码：158-167
- 作者机构：
  
  1. 吉林大学通信工程学院，吉林长春 130012
  2. 东北电力大学信息工程学院，吉林吉林 132012
- 作者简介：
  
  [ "李华亮（1990-），男，吉林长春人，吉林大学硕士生，主要研究方向为无线定位技术。" ]
  [ "钱志鸿（1957-），男，吉林长春人，吉林大学教授、博士生导师，主要研究方向为基于物联网、D2D、Wi-Fi、RFID 等无线网络与通信技术。" ]
  [ "田洪亮（1981-），男，吉林省吉林市人，吉林大学博士生，东北电力大学讲师，主要研究方向为无线个域网。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61371092);国家自然科学基金资助项目(61401175);国家自然科学基金资助项目(61540022);吉林省科技厅基金资助项目(20140204019GX);长春市重大科技攻关计划基金资助项目(2014026/14KG021);吉林大学研究生创新基金资助项目(2019091)
- DOI：10.11959/j.issn.1000-436x.2017018
  中图分类号： TN92
- 网络出版日期：2017-01，
  
  纸质出版日期：2017-01-25
- 稿件说明：
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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.

Hua-liang LI, Zhi-hong QIAN, Hong-liang TIAN. Research on indoor localization algorithm based on kernel principal component analysis[J]. Journal on communications, 2017, 38(1): 158-167.
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. DOI： 10.11959/j.issn.1000-436x.2017018.

Hua-liang LI, Zhi-hong QIAN, Hong-liang TIAN. Research on indoor localization algorithm based on kernel principal component analysis[J]. Journal on communications, 2017, 38(1): 158-167. DOI： 10.11959/j.issn.1000-436x.2017018.

摘要

提出了一种基于核函数特征提取(KPCA

kernel principal component analysis)的室内定位算法。该算法在离线阶段使用核函数特征提取方法训练原始位置指纹(OLF

original location fingerprint)，提取原始位置指纹的非线性特征，可以有效地利用各个接入节点(AP

access point)的接收信号强度信息；而在线阶段使用一种改进的加权k近邻 (IWKNN

improved weight k-nearest neighbor)算法，自主选择近邻数进行位置估计。实验结果表明，提出的算法在平均误差和定位准确率方面优于其他的室内定位算法，并且该算法需要更少的接收信号强度（RSS

received signal strength）采集次数和AP个数。

Abstract

An indoor localization algorithm based on kernel principal component analysis (KPCA) was proposed.It applied KPCA to train the original location fingerprint (OLF) and extract the nonlinear feature of the OLF data at the offline stage

such that the information of all AP was more efficiently utilized.At the online stage

an improved weight k-nearest neighbor algorithm for positioning which could automatically choose neighbors was proposed.The experiments were carried out in a realistic WLAN environment.The results show that the algorithm outperforms the existing methods in terms of the mean error and localization accuracy.Moreover

it requires less times of RSS acquisition and AP number.

关键词

Keywords

references

钱志鸿 , 王义君 . 面向物联网的无线传感器网络综述 [J ] . 电子与信息学报 , 2013 , 35 ( 1 ): 215 - 227 .

QIAN Z H , WANG Y J . Internet of things-oriented wireless sensor networks review [J ] . Journal of Electronics ＆ Information Technology , 2013 , 35 ( 1 ): 215 - 227 .

钱志鸿 , 王雪 . 面向 5G 通信网的 D2D 技术综述 [J ] . 通信学报 , 2016 , 37 ( 7 ): 1 - 14 .

QIAN Z H , WANG X . Reviews of D2D technology for 5G communication networks [J ] . Journal on Communications , 2016 , 37 ( 7 ): 1 - 14 .

KOLODZIEJ K W , HJELM J . Local positioning systems:LBS applications and services [M ] // Local Positioning System.LBS Applications and Services . 2006 : 101 - 158 .

MA L , XU Y . Received signal strength recovery in green WLAN indoor positioning system using singular value thresholding [J ] . Sensors , 2015 , 15 ( 1 ): 1292 - 1311 .

JAIN V K , TAPASWI S , SHUKLA A . Performance analysis of received signal strength fingerprinting based distributed location estimation system for indoor WLAN [J ] . Wireless Personal Communications , 2013 , 70 ( 1 ): 113 - 127 .

SCHMIDT E , AKOPIAN D . Indoor positioning system using WLAN channel estimates as fingerprints for mobile devices [C ] // IS＆T/SPIE Electronic Imaging,International Society for Optics and Photonics , 2015 :94110R-94110R-9.

DENG Y B , ZHI A X , LIN M . WLAN indoor positioning algorithm based on KDDA and SVR [J ] . Journal of Electronics ＆ Information Technology , 2011 , 4 : 23 .

HE S , CHAN S H G . Wi-Fi fingerprint-based indoor positioning:recent advances and comparisons [J ] . IEEE Communications Surveys＆ Tutorials , 2016 , 18 ( 1 ): 466 - 490 .

MENGUAL L,MARBÁN O , EIBE S . Clustering-based location in wireless networks [J ] . Expert Systems with Applications , 2010 , 37 ( 9 ): 6165 - 6175 .

WEN Y , TIAN X , WANG X , et al . Fundamental limits of RSS fingerprinting based indoor localization [C ] // 2015 IEEE Conference on Computer Communications (INFOCOM) . IEEE , 2015 : 2479 - 2487 .

BAHL P , PADMANABHAN V N . RADAR:An in-building RF-based user location and tracking system [C ] // INFOCOM 2000.Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies . 2000 , 2 : 775 - 784 .

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

FANG S H , LIN T N . Principal component localization in indoor WLAN environments [J ] . IEEE Transactions on Mobile Computing , 2012 , 11 ( 1 ): 100 - 110 .

MARTÍNEZ A M , KAK A C . PCA versus LDA [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2001 , 23 ( 2 ): 228 - 233 .

DE SA J P M . Pattern recognition:concepts,methods and applications [M ] . Springer Science ＆ Business Media , 2012 .

DING M , FAN G . Articulated and generalized Gaussian kernel correlation for human pose estimation [J ] . IEEE Transactions on Image Processing , 2016 , 25 ( 2 ): 776 - 789 .

KING T , HAENSELMANN T , EFFELSBERG W.On-demand fingerprint selection for 802 . 11-based positioning systems [C ] // World of Wireless,Mobile and Multimedia Networks,2008 ( WoWMoM 2008) . 2008 : 1 - 8 .

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