Indoor positioning algorithm research based on the typicality judgment of RSS

Jia-ying WU; Wei-hong XU; Shun-ming CHEN; Ping LI

doi:10.3969/j.issn.1000-436x.2014.z2.019

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Indoor positioning algorithm research based on the typicality judgment of RSS

Papers | 更新时间：2024-06-05

- Indoor positioning algorithm research based on the typicality judgment of RSS
- Journal on Communications Vol. 35, Issue Z2, Pages: 140-146(2014)
- 作者机构：
  
  1. 南京理工大学计算机与工程学院，江苏南京 210094
  2. 长沙理工大学计算机与通信工程学院，湖南长沙 410114
- 作者简介：
- 基金信息：
  
  Project Supported by the Research Foundation of Hunan Province(14A004)
- DOI：10.3969/j.issn.1000-436x.2014.z2.019
  CLC： TP391
- Online First：2014-11，
  
  Published：30 November 2014
- 稿件说明：
移动端阅览
Jia-ying WU, Wei-hong XU, Shun-ming CHEN, et al. Indoor positioning algorithm research based on the typicality judgment of RSS[J]. Journal on Communications, 2014, 35(Z2): 140-146.
DOI：

Jia-ying WU, Wei-hong XU, Shun-ming CHEN, et al. Indoor positioning algorithm research based on the typicality judgment of RSS[J]. Journal on Communications, 2014, 35(Z2): 140-146. DOI： 10.3969/j.issn.1000-436x.2014.z2.019.

摘要

在基于RSS指纹集的定位算法中，相似样本集的质量，是影响定位精度的一个关键性因素；而待定位点的RSS向量，则是影响相似样本点质量的一个重要元素。通过对D-RSS分布规律分析，提出了RSS典型性的概念，并且提出了基于RSS典型性判定的室内定位算法。该算法根据RSS的典型性特征与有效的相似样本点之间的关系，提出了 RSS 典型性的辨别方法以及与典型性相关的动态 K 值。通过实验证明，该算法不仅能完整地找出有效的相似样本点，排除非实质性相似点的干扰，而且在不同的定位场景中具有较强的适应性，同时具有较高的定位精度。

Abstract

In the process of indoor location based on RSS fingerprint

the quality of the obtained similar point set is a key factor for a successful position.And the locating point’s RSS is an important reason which affects the quality of the similar point set.By analyzing the distribution of D-RSS

the concept of RSS’s typicality was proposed firstly

and an indoor localization algorithm based on typicality judgment of RSS was also presented.According to the principle that the RSS values and the effective similar sample points

a typicality discrimination method for RSS values and a self-adapting K value were presented.Confirmed by the experiments

the algorithm not only can find the effective similarity sample points completely

but also can eliminate the non-substantive similarities points

and then can adapt to the different scenes

then have the higher positioning accuracy.

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Keywords

references

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