iBeacon网络下的区域化双层定位体系
收稿日期: 2016-07-28
修回日期: 2016-10-19
网络出版日期: 2017-01-30
基金资助
国家“863”高技术研究发展计划基金（No.2013AA03A1121，No.2013AA03A1122）；上海市教委重点学科基金（No.J50104）；上海市科委项目培育基金（No.D.72-0107-00-024）资助
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
针对许多传统室内大空间定位方法难以同时提高定位实时性和精度的问题，提出一种iBeacon网络下的区域化双层定位体系.该体系由两种优化后的室内定位算法与iBeacon双层定位架构组成.前者通过空间区域化概率匹配算法实现快速区域定位，利用区域内加权质心算法实现高精度区域内定位；后者通过iBeacon识别码对定位节点进行两级划分，利用两级节点的不同组合实现不同的定位层次.该体系通过iBeacon双层定位架构将处于不同定位层次的两种室内定位算法相结合，可同时提升实时性和精度.实验表明，在相近定位精度条件下，所提定位体系的实时性比K最近邻算法、加权K最近邻算法分别提高55.29%和54.18%.定位精度比基于RSSI的四边测距改进加权质心算法提高37.35%.该体系具有高精度和低成本优势，可广泛用于大型建筑室内导航及行人轨迹探测等领域，经济和社会应用价值高.
姚维强, 张金艺, 鲍深, 梁滨 . iBeacon网络下的区域化双层定位体系[J]. 应用科学学报, 2017 , 35(1) : 51 -62 . DOI: 10.3969/j.issn.0255-8297.2017.01.006
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.
[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)
/
| 〈 |
|
〉 |
