应用于WiFi室内定位的自适应仿射传播聚类算法

胡久松; 刘宏立; 肖郭璇; 徐琨

doi:10.11999/JEIT180186

应用于WiFi室内定位的自适应仿射传播聚类算法

doi: 10.11999/JEIT180186

胡久松^{1, 2},
刘宏立^1, ,,
肖郭璇³,
徐琨¹

1.
湖南大学电气与信息工程学院长沙 410006
2.
湖南工业大学交通工程学院株洲 412000
3.
国家电网浙江乐清市供电公司乐清 325600

基金项目: 中央国有资本经营预算项目(财企[2013]470号)；国家自然科学基金(61771191)

详细信息

作者简介:
胡久松：男，1987年生，博士生，研究方向为室内定位、机器学习

刘宏立：男，1963年生，教授，博士生导师，主要研究方向为无线传感网络、现代通信理论和移动通信系统

肖郭璇：女，1989年生，工程师，研究方向为智能电网

徐琨：男，1979年生，博士生，研究方向为无线传感网络、移动通信

通讯作者:
刘宏立　 hongliliu@hnu.edu.cn

中图分类号: TP391
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出版历程
- 收稿日期: 2018-02-10
- 修回日期: 2018-09-03
- 网络出版日期: 2018-09-10
- 刊出日期: 2018-12-01

Adaptive Affine Propagation Clustering Algorithm for WiFi Indoor Positioning

Jiusong HU^{1, 2},
Hongli LIU^{1
, ,},
Guoxuan XIAO³,
Kun XU¹

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410006, China
2.
College of Traffic Engineering, Hunan University of Technology, Zhuzhou 412000, China
3.
State Grid Yueqing Electric Power Supply Company, Yueqing, 325600, China

Funds: The Central State-Owned Capital Management and Budget Project (2013-470), The National Natural Science Foundation of China (61771191)

摘要

摘要: 在室内覆盖的大量的WiFi信号可以用来室内定位。尽管很多WiFi室内定位技术被提出，但其定位精度仍然未达到实际应用的需求。针对这个问题，该文提出一种自适应仿射传播聚类(AAPC)算法用以提高WiFi指纹的聚类质量，从而提高定位精度。AAPC算法通过动态调整参数生成不同的聚类结果，然后采用聚类有效性指标筛选出其中最佳的。采集大量真实环境数据进行试验，试验结果表明采用AAPC算法产生的聚类结果具有更高的定位精度。
- WiFi室内定位 /
- 自适应仿射传播聚类 /
- 聚类有效性指标
Abstract: There are a large number of indoor WiFi signals which can be used for indoor positioning. Although many WiFi indoor positioning technology is proposed, it's positioning accuracy still does not meet the actual application requirements. For this problem, an Adaptive Affinity Propagation Clustering (AAPC) algorithm is proposed to improve the clustering quality of WiFi fingerprint, thus improving the positioning accuracy. The AAPC algorithm generates different clustering results by dynamically adjusting parameters, then cluster validity indices are used to select the best ones. A large number of real environmental data are collected and tested. The experimental results show that the clustering results generated by AAPC algorithm have higher positioning accuracy.
- WiFi indoor positioning /
- Adaptive Affine Propagation Clustering (AAPC) algorithm /
- Cluster validity indices

HTML全文

图 1 系统框图

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图 2 生成指定目标聚类数的自适应APC算法流程

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图 3 试验环境的平面图以及参考点分布

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图 4 不同聚类算法的聚类结果

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图 5 不同聚类算法的定位结果

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表 1 UCI数据集

数据集	类型	样本数	属性个数	类数
iris	real	150	4	3
air	real	359	64	3
sonar	real	208	60	2
glass	real	214	9	6
wine	real	178	12	3
heart	real	270	13	2
zoo	artificial	101	16	7
ionosphere	real	351	34	2
vote	artificial	435	16	2
vowel	real	528	10	11
diabetes	real	768	8	2

下载: 导出CSV

表 2 3种算法的对比结果

数据集	是否收敛			聚类数			真实	时间(s)
数据集	A	B	C	A	B	C	真实	A	B	C
iris	√	√	√	2	2	2	3	44.6	15.0	1.0
air	√	×	√	2	×	2	3	275.6	×	8.4
sonar	√	×	√	3	×	3	2	96	×	2.5
glass	√	×	√	4	×	5	6	133	×	6.7
wine	√	√	√	2	2	2	3	53.9	32	1.7
heart	√	×	√	2	×	3	2	146.6	×	5.3
zoo	√	×	√	6	×	4	7	48.1	×	0.9
ionosphere	×	×	√	×	×	4	2	×	×	0.8
vote	√	×	√	2	×	2	2	767.6	×	34.7
vowel	√	×	√	22	75	18	11	774.4	576.6	36.9
diabetes	√	×	√	2	×	2	2	1670	×	105.5

下载: 导出CSV

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