應(yīng)用于WiFi室內(nèi)定位的自適應(yīng)仿射傳播聚類算法

胡久松; 劉宏立; 肖郭璇; 徐琨

doi:10.11999/JEIT180186

應(yīng)用于WiFi室內(nèi)定位的自適應(yīng)仿射傳播聚類算法

doi: 10.11999/JEIT180186 cstr: 32379.14.JEIT180186

胡久松^{1, 2},
劉宏立^1, ,,
肖郭璇³,
徐琨¹

1.
湖南大學(xué)電氣與信息工程學(xué)院 ??長沙 ??410006
2.
湖南工業(yè)大學(xué)交通工程學(xué)院 ??株洲 ??412000
3.
國家電網(wǎng)浙江樂清市供電公司 ??樂清 ??325600

基金項目: 中央國有資本經(jīng)營預(yù)算項目(財企[2013]470號)；國家自然科學(xué)基金(61771191)

詳細信息

作者簡介:
胡久松：男，1987年生，博士生，研究方向為室內(nèi)定位、機器學(xué)習(xí)

劉宏立：男，1963年生，教授，博士生導(dǎo)師，主要研究方向為無線傳感網(wǎng)絡(luò)、現(xiàn)代通信理論和移動通信系統(tǒng)

肖郭璇：女，1989年生，工程師，研究方向為智能電網(wǎng)

徐琨：男，1979年生，博士生，研究方向為無線傳感網(wǎng)絡(luò)、移動通信

通訊作者:
劉宏立　 hongliliu@hnu.edu.cn

中圖分類號: TP391
計量
- 文章訪問數(shù): 1744
- HTML全文瀏覽量: 624
- PDF下載量: 56
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-02-10
- 修回日期: 2018-09-03
- 網(wǎng)絡(luò)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: 在室內(nèi)覆蓋的大量的WiFi信號可以用來室內(nèi)定位。盡管很多WiFi室內(nèi)定位技術(shù)被提出，但其定位精度仍然未達到實際應(yīng)用的需求。針對這個問題，該文提出一種自適應(yīng)仿射傳播聚類(AAPC)算法用以提高WiFi指紋的聚類質(zhì)量，從而提高定位精度。AAPC算法通過動態(tài)調(diào)整參數(shù)生成不同的聚類結(jié)果，然后采用聚類有效性指標(biāo)篩選出其中最佳的。采集大量真實環(huán)境數(shù)據(jù)進行試驗，試驗結(jié)果表明采用AAPC算法產(chǎn)生的聚類結(jié)果具有更高的定位精度。
- WiFi室內(nèi)定位 /
- 自適應(yīng)仿射傳播聚類 /
- 聚類有效性指標(biāo)
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

圖 1 系統(tǒng)框圖

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圖 2 生成指定目標(biāo)聚類數(shù)的自適應(yīng)APC算法流程

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圖 3 試驗環(huán)境的平面圖以及參考點分布

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圖 4 不同聚類算法的聚類結(jié)果

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圖 5 不同聚類算法的定位結(jié)果

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表 1 UCI數(shù)據(jù)集

數(shù)據(jù)集	類型	樣本數(shù)	屬性個數(shù)	類數(shù)
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

下載: 導(dǎo)出CSV

表 2 3種算法的對比結(jié)果

數(shù)據(jù)集	是否收斂			聚類數(shù)			真實	時間(s)
數(shù)據(jù)集	A	B	C	A	B	C	真實	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

下載: 導(dǎo)出CSV

參考文獻(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. 基于流形插值數(shù)據(jù)庫構(gòu)建的WLAN室內(nèi)定位算法[J]. 電子與信息學(xué)報, 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.

李麗娜, 馬俊, 龍躍, 等. 基于LANDMARC與壓縮感知的雙段式室內(nèi)定位算法[J]. 電子與信息學(xué)報, 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.

王開軍, 張軍英, 李丹, 等. 自適應(yīng)仿射傳播聚類[J]. 自動化學(xué)報, 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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