Localization of Double Layer Location System Based on IBeacon Network
Received date: 2016-07-28
Revised date: 2016-10-19
Online published: 2017-01-30
In many traditional indoor large space localization method, it is difcult to improve both positioning accuracy and real-time performance.This paper proposes a localization system based on iBeacon network.The system composes of two optimized indoor positioning algorithms, and has an iBeacon dual layer positioning architecture.The former achieves rapid location with an algorithm that matchesa region of space probability, and achieves high precision within each region in the area with a weighted centroid algorithm.The latter, based on the iBeacon identifcation code, is divided into two levels of node localization.Different levels of positioning is achieved by using different combinations of these nodes.In the positioning process, the system uses the iBeacon double layer positioning architecture at different levels of the two positioning algorithms to improve accuracy of real-time positioning.Experimental results show that, with similar accuracy, the proposed system improves the real-time performance by 55.29% and 54.18% respectively compared with K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN).Positioning accuracy is improved by 37.35% compared with an improved weighted centroid algorithm based on RSSI.The proposed system has high economic and social values as it can be used for navigation in large buildings and detect pedestrian paths.
YAO Wei-qiang, ZHANG Jin-yi, BAO Shen, LIANG Bin . Localization of Double Layer Location System Based on IBeacon Network[J]. Journal of Applied Sciences, 2017 , 35(1) : 51 -62 . DOI: 10.3969/j.issn.0255-8297.2017.01.006
[1] Wenjun G U, Aminikashani M, Deng P. Impact of multipath reflections on the performance of indoor visible light positioning systems[J]. Mathematics, 2015, 17(3):251-266.
[2] Gentner C, Jost T. Indoor positioning using time difference of arrival between multipath components[C]//International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2013:1-10.
[3] Sunantasaengtong P, Chivapreecha S. Mixed K-means and GA-based weighted distance fngerprint algorithm for indoor localization system[C]//IEEE Region 10 Conference TENCON 2014-2014. 2014:1-5.
[4] Huang C H, Lee L H, Ho C C, Wu L L, Lai Z H. Real-time RFID indoor positioning system based on Kalman-flter drift removal and Heron-bilateration location estimation[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(3):728-739.
[5] Jin P F, Liu P X, Chen Y J. An improved algorithm of weighted centroid algorithm based on RSSI[C]//Advanced Materials Research. Germany:Trans Tech Publications, 2014, 926:2955-2958.
[6] 陈国良,张言哲,汪云甲,孟晓林. WiFi-PDR室内组合定位的无迹卡尔曼滤波算法[J]. 测绘学报,2015(12):1314-1321. Chen G L, Zhang Y Z, Wang Y J, Meng X L. Unscented Kalman flter algorithm for WiFi-PDR integrated indoor positioning[J]. Acta Geod Cartogr Sin, 2015(12):1314-1321. (in Chinese)
[7] 李娟娟,张金艺,张秉煜. 蓝牙4.0标准规范下的模糊指纹定位算法[J]. 上海大学学报(自然科学版),2013, 19(2):126-131. Li J J, Zhang J Y, Zhang B Y. Fuzzy fngerprint location for bluetooth specifcation version 4.0[J]. Journal of Shanghai University (Natural Science), 2013, 19(2):126-131. (in Chinese)
[8] Chen L H, Wu H K, Jin M H, Chen G H. Intelligent fusion of Wi-Fi and inertial sensor-based positioning systems for indoor pedestrian navigation[J]. IEEE Sensors Journal, 2014, 14(11):4034-4042. (in Chinese)
[9] Lu X, Liu H, Liu F. A novel algorithm for enhancing accuracy of indoor position estimation[C]//201411th World Congress on Intelligent Control and Automation (WCICA), IEEE, 2014:5528-5533.
[10] Blumenthal J, Grossmann R, Golatowski F, Timmermann D. Weighted centroid localization in zigbee-based sensor networks[C]//IEEE International Symposium on Intelligent Signal Processing, 2007:1-6.
[11] 曹文静. 基于iOS的室内定位体系的研究与实现[D]. 成都:四川师范大学,2015:12-21.
[12] 杨萌,修春娣,邹坤,杨东凯,刘源. 一种基于感知概率的室内定位匹配算法[J]. 导航定位学报,2014(4):49-53. Yang M, Xiu C D, Zhou K, Yang D K, Liu Y. A sensing probability-based matching algorithm for WiFi indoor positioning systems[J]. Journal of Navigation and Positioning, 2014(4):49-53. (in Chinese)
[13] Xie S, Hu Y, Wang Y. Weighted centroid localization algorithm based on least square for wireless sensor networks[C]//IEEE International Conference on Consumer Electronics-China. IEEE, 2014:1-4.
[14] 朱忠记,何熊熊,章晓. 基于RSSI的四边测距改进加权质心定位算法[J]. 杭州电子科技大学学报,2014, 34(1):17-20. Zhu Z J, He X X, Zhang X. The quadrilateral and improved weighted centroid localization algorithm based on RSSI[J]. Journal of Hangzhou Dianzi University, 2014, 34(1):17-20. (in Chinese)
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