计算机工程与应用2019，Vol.55Issue(1)：29-34,46,7.DOI:10.3778/j.issn.1002-8331.1809-0143
基于深度学习的Wi-Fi与iBeacon融合的室内定位方法
Indoor Localization Based on Deep Learning Using Wi-Fi and iBeacon
摘要
Abstract
Aiming at the problem that the traditional indoor fingerprint localization algorithm has low positioning accuracy and is easily affected by the environment, an indoor localization algorithm based on deep learning using Wi-Fi and iBeacon is proposed. Signal strength of each AP and iBeacon is collected at each reference point in offline phase, and is used to train the stacked auto-encoder which is used to extract features from a large number of signal strength samples with noise. These features are used to construct the fingerprint database. The features of the point to be measured can be obtained by the stacked auto-encoder in online phase. Then these features are matched in fingerprint database. The position of the point to be measured is estimated by the nearest neighbor algorithm. Experimental results show that the proposed indoor localization algorithm has higher localization accuracy.关键词
室内定位/深度学习/堆叠自动编码机/近邻算法/iBeacon/Wi-FiKey words
indoor localization/deep learning/stacked auto-encoder/nearest neighbor algorithm/iBeacon/Wi-Fi分类
信息技术与安全科学引用本文复制引用
薛伟,陈璟,张熠..基于深度学习的Wi-Fi与iBeacon融合的室内定位方法[J].计算机工程与应用,2019,55(1):29-34,46,7.基金项目
江苏省青年科学基金(No.BK20150159) （No.BK20150159）
江苏省研究生科研与实践创新计划项目(No.SJCX17_0509). （No.SJCX17_0509）
