Research on indoor localization algorithm based on kernel principal component analysis

Hua-liang LI; Zhi-hong QIAN; Hong-liang TIAN

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

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Research on indoor localization algorithm based on kernel principal component analysis

Correspondences | 更新时间：2024-06-05

- Research on indoor localization algorithm based on kernel principal component analysis
- Journal on Communications Vol. 38, Issue 1, Pages: 158-167(2017)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61371092);The National Natural Science Foundation of China(61401175);The National Natural Science Foundation of China(61540022);Research Project of Science and Technology Department of Jilin Province(20140204019GX);Key Science and Technology Program of Changchun(2014026/14KG021);Project Supported by Graduate Innovation Fund of Jilin University(2019091)
- DOI：10.11959/j.issn.1000-436x.2017018
  CLC： TN92
- Online First：2017-01，
  
  Published：25 January 2017
- 稿件说明：
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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：

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

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