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

参考文献(22)

DAVIDSON P and PICHE R. A survey of selected indoor positioning methods for smartphones[J]. IEEE Communications Surveys and Tutorials, 2017, 19(2): 1347–1370 doi: 10.1109/comst.2016.2637663

ZHANG Weile, YIN Qinye, CHEN Hongyang, et al. Distributed angle estimation for localization in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(2): 527–537 doi: 10.1109/TWC.2012.121412.111346

LIU Bin, CHEN Hongyang, ZHONG Ziguo, et al. Asymmetrical round trip based synchronization-free localization in large-scale underwater sensor networks[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3532–3542 doi: 10.1109/TWC.2010.090210.100146

CHEN Hongyang, LIU Bin, HUANG Pei, et al. Mobility-assisted node localization based on TOA measurements without time synchronization in wireless sensor networks[J]. Mobile Networks&Applications, 2012, 17(1): 90–99 doi: 10.1007/s11036-010-0281-3

HOSSAIN A K M M and SOH W. A survey of calibration-free indoor positioning systems[J]. Computer Communications, 2015, 66: 1–13 doi: 10.1016/j.comcom.2015.03.001

FENG Chen, AU W S A, VALAEE S, et al. Received-signal-strength-based indoor positioning using compressive sensing[J]. IEEE Transactions on Mobile Computing, 2012, 11(12): 1983–1993 doi: 10.1109/tmc.2011.216

周牧, 唐云霞, 田增山, 等. 基于流形插值数据库构建的WLAN室内定位算法[J]. 电子与信息学报, 2017, 39(8): 1826–1834 doi: 10.11999/JEIT161269

ZHOU Mu, TANG Yunxia, TIAN Zengshan, et al. WLAN indoor localization algorithm based on manifold interpolation database construction[J]. Journal of Electronics&Information Technology, 2017, 39(8): 1826–1834 doi: 10.11999/JEIT161269

BAI Sidong and WU Tong. Analysis of K-means algorithm on fingerprint based indoor localization system[C]. IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. Chengdu, China, 2013: 44–48.

ZHANG Liwen, WANG Yunjia, and WANG Xingfeng. Affinity propagation clustering for fingerprinting database in indoor localization[J]. Bulletin of Surveying and Mapping, 2014(12): 36–39 doi: 10.13474/j.cnki112246.2014.0392

BAHL P and PADMANABHAN V N. RADAR: An in-building RF-based user location and tracking system[C]. Proceedings-IEEE INFOCOM, TelAviv, Israel, 2000, 2: 775–784.

YOUSSEF M and AGRAWALA A. The horus WLAN location determination system[C]. Proceedings of the Third International Conference on Mobile Systems, Applications, and Services (MobiSys 2005). Seattle, USA, 2005: 205–218.

李丽娜, 马俊, 龙跃, 等. 基于LANDMARC与压缩感知的双段式室内定位算法[J]. 电子与信息学报, 2016, 38(7): 1631–1637 doi: 10.11999/JEIT151050

LI Lina, MA Jun, LONG Yue, et al. Double stage indoor localization algorithm based on LANDMARC and compressive sensing[J]. Journal of Electronics&Information Technology, 2016, 38(7): 1631–1637 doi: 10.11999/JEIT151050

CASO G, NARDIS L D, and BENEDETTO M G D. A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning[J]. Sensors, 2015, 15(11): 27692–27720 doi: 10.3390/s151127692

AU A W S, FENG Chen, VALAEE S, et al. Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device[J]. IEEE Transactions on Mobile Computing, 2013, 12(10): 2050–2062 doi: 10.1109/TMC.2012.175

FREY B J and DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972–976 doi: 10.1126/science.1136800

FREY L P and STATISTICAL I G. Affinity propagation (University of Toronto) [OL]. avail-able: https://www.psi.toronto.edu/affinitypropagation/software/, 2018.

YU Jian and JIA Caiyan. Convergence analysis of affinity propagation[C]. International Conference on Knowledge Science, Engineering and Management. Berlin, Germany, 2009: 54–65.

王开军, 张军英, 李丹, 等. 自适应仿射传播聚类[J]. 自动化学报, 2008, 33(12): 1242–1246 doi: 10.16383/j.aas.2007.12.017

WANG Kaijun, ZHANG Junying, LI Dan, et al. Adaptive affinity propagation clustering[J]. Acta Automatica Sinica, 2008, 33(12): 1242–1246 doi: 10.16383/j.aas.2007.12.017

YU Jian and CHENG Qiansheng. The upper bound of the optimal number of clusters in fuzzy clustering[J]. Science in China Series:Information Sciences, 2001, 44(2): 119–125 doi: 10.1007/bf02713970

ARBELAITZ O, GURRUTXAGA I, MUGUERZA J, et al. An extensive comparative study of cluster validity indices[J]. Pattern Recognition, 2013, 46(1): 243–256 doi: 10.1016/j.patcog.2012.07.021

SUROSO D J, CHERNTANOMWONG P, SOORAKSA P, et al. Location fingerprint technique using fuzzy C-means clustering algorithm for indoor localization[C]. TENCON 2011–2011 IEEE Region 10 Conference. IEEE, Bali, Indonesia, 2012: 88–92.

BLAKE C L and MERZ C J. UCI repository of machine learning databases (University of California) [OL], available: http://archive.ics.uci.edu/ml/, 2018.

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