Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi

TIAN Hongliang; QIAN Zhihong; LIANG Xiao; WANG Yijun; WANG Xue

doi:10.11918/j.issn.0367-6234.201610104

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Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi

更新时间：2026-10-19

- Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi
- Vol. 49, Issue 5, Pages: 2017-5(2017)
- 作者机构：
  
  1. 吉林大学通信工程学院,长春,130012
  2. 东北电力大学信息工程学院,吉林,吉林,132012
  3. 长春理工大学电子信息工程学院,长春,130022
- 作者简介：
- 基金信息：
- DOI：10.11918/j.issn.0367-6234.201610104
  CLC： TP393
- Published：2017
- 稿件说明：
移动端阅览
TIAN Hongliang, QIAN Zhihong, LIANG Xiao, et al. Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi[J]. 2017, 49(5): 2017-5.
DOI：

TIAN Hongliang, QIAN Zhihong, LIANG Xiao, et al. Discrete degree WKNN location fingerprinting algorithm based on Wi-Fi[J]. 2017, 49(5): 2017-5. DOI： 10.11918/j.issn.0367-6234.201610104.

摘要

为改善加权K近邻位置指纹定位算法在室内环境复杂时的定位性能，提出一种以位置指纹离散度作为权值参考的改进加权K近邻位置指纹定位算法.算法在离线位置指纹数据库建立阶段采用K-means聚类算法对位置指纹进行聚类，来降低搜索位置指纹库的计算量.从离线位置指纹库中选取K个与在线实测Wi-Fi信号强度信息最相似的位置指纹，比较其离散程度，将离散程度小的位置指纹赋予较高的加权系数，以减小原加权K近邻算法在室内复杂环境信号强度随距离变化较大情况下带来的位置估算误差.对离散度加权K近邻算法时间复杂度的分析表明，其计算量小于原加权K近邻算法；实际环境实验结果表明，离散度加权K近邻算法具有更高的定位精度，且定位误差波动较小.

Abstract

To improve the localization performance of the WKNN location fingerprinting algorithm when the indoor environment is complex

an improved WKNN location fingerprinting algorithm—Discrete Degree Weighted K-Nearest Neighbor (DD-WKNN) is proposed

which takes the dispersion of location fingerprints as the weight reference. The K-means clustering algorithm is used to cluster the location fingerprints when the offline location fingerprint database is established

which reduces the computational complexity of searching the location fingerprint database. K location fingerprints which are most similar to online measured RSSIs are selected from the offline location fingerprint database

and the discrepancy degrees are compared. A higher weighting coefficient is assigned to the position fingerprint with a small degree of dispersion

which reduces the error of position estimation caused by the original WKNN algorithm when the signal strength of the indoor environment changes greatly with distance. The analysis of the time complexity of DD-WKNN algorithm shows that its computational complexity is less than that of the original WKNN algorithm. The experimental results show that the DD-WKNN algorithm has a higher positioning accuracy and the positioning error fluctuates less.

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