PCA clustering algorithm for indoor positioning in WLAN

Mingji YANG; Kaiyi LIU; Dan SHAO

doi:10.11959/j.issn.1000-0801.2016186

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PCA clustering algorithm for indoor positioning in WLAN

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- PCA clustering algorithm for indoor positioning in WLAN
- Telecommunications Science Vol. 32, Issue 7, Pages: 21-26(2016)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2016186
  CLC： TN929.5
- Published Online：2016-07，
  
  Published：15 July 2016
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Mingji YANG, Kaiyi LIU, Dan SHAO. PCA clustering algorithm for indoor positioning in WLAN[J]. Telecommunications science, 2016, 32(7): 21-26.
DOI：

Mingji YANG, Kaiyi LIU, Dan SHAO. PCA clustering algorithm for indoor positioning in WLAN[J]. Telecommunications science, 2016, 32(7): 21-26. DOI： 10.11959/j.issn.1000-0801.2016186.

摘要

在WLAN室内定位系统中，针对接收信号强度（RSS）的时变特征降低室内定位精度的问题，提出一种基于主成分分析（PCA）白化RSS的聚类算法，该算法首先对信号强度进行PCA白化处理，去除RSS信息的相关性，提高聚类中心的可靠性和合理性，然后通过K-means聚类方式对RSS信息进行聚类，能够有效地提高聚类精度，以此来提高定位精度。实验结果表明，该算法相比于没有经过PCA的传统聚类算法，能够使定位误差在2 m内的概率提高44.8%，性能更优良。

Abstract

In WLAN indoor location system

aiming at the problem of time-varying characteristic of received signal strength (RSS) which reduces indoor positioning accuracy

a clustering algorithm based on principal component analysis (PCA) albino RSS was put forward.The algorithm firstly treated the RSS with PCA whitening treatment to remove the correlation and improve reliability and rationality of the cluster centers.Then

K-means clustering method was used to cluster the RSS and the clustering accuracy was improved effectively

so as to improve positioning accuracy.Experimental results show that compared with the traditional clustering algorithm without PCA

probability of positioning error within 2 meters has improved 44.8% in positioning accuracy

and the performance of positioning system has been more excellent.

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references

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