(中国科学院 计算技术研究所 普适计算研究中心, 北京 100190;北京市移动计算与新型终端重点实验室, 北京 100190;中国科学院大学 信息科学与工程学院, 北京 100190)
Device Adaptive Wireless Signal Feature Extraction and Localization Method
GU Yang,JIANG Xin-Long,LIU Jun-Fa,CHEN Yi-Qiang
(Department of Pervasive Computing, Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China;Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China;Department of Information Science and Engineering, University of Chinese Academy of Sciences, Beijing 100190, China)
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Received:June 15, 2013    Revised:August 21, 2013
> 中文摘要: 近年来,基于Wi-Fi的无线定位研究日益受到关注.然而,在实际应用中,由不同终端设备的差异引起的定位偏差是一个重要问题.针对此问题提出了一种免标定、无监督的SSDR(signal strength difference ratio)解决方法.考虑采集训练数据的设备和测试数据的设备之间信号存在差异,首先将信号指纹特征进行去线性处理以获取新的特征;然后结合AP(access point)对定位结果的影响,提出了基于AP影响因子计算距离的标准;最后根据新的特征和距离计算准则消除不同设备之间的差异以实现定位.在真实的室内无线环境下的实验结果表明,所提出的SSDR方法相比于传统的直接基于信号强度和欧式距离计算准则的定位方法而言,可以提高10%~20%的定位精度,增强了无线定位系统的实际可用性.
Abstract:In recent years, research on Wi-Fi based indoor localization draws increasing attention. However, in practical applications, the localization error caused by device variance is a severe problem. In this paper, a new calibration-free and unsupervised method, SSDR (Signal Strength Difference Ratio) is proposed to solve this issue. Considering the signal variance between training devices and testing devices, SSDR first removes the linear effect of fingerprint to get new features. It then puts forward a distance calculation criterion with AP impact factor according to the effect of AP. Finally SSDR eliminates the variance of devices and realizes indoor localization based on the new features and distance calculation criterion. The experiment deployed in real indoor wireless environment shows, compared with traditional indoor localization methods, the proposed SSDR can increase the indoor localization accuracy by 10%~20%, which greatly improves the practical usability of indoor localization system.
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基金项目:国家自然科学基金(61173066);广东省中国科学院全面战略合作项目(2011A090100001) 国家自然科学基金(61173066);广东省中国科学院全面战略合作项目(2011A090100001)
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GU Yang,JIANG Xin-Long,LIU Jun-Fa,CHEN Yi-Qiang.Device Adaptive Wireless Signal Feature Extraction and Localization Method.Journal of Software,2014,25(S2):12-20