Feature Extraction Based on Manifold Learning for Radio Fingerprint

Q. Pu, Tianshu Tang, J. Ng, Fawen Zhang
{"title":"Feature Extraction Based on Manifold Learning for Radio Fingerprint","authors":"Q. Pu, Tianshu Tang, J. Ng, Fawen Zhang","doi":"10.1109/WCSP.2019.8928088","DOIUrl":null,"url":null,"abstract":"Wireless Local Area Network (WLAN) fingerprinting has been extensively studied for indoor localization due to the pervasive facilities. Conventional fingerprint database is composed of a set of raw Received Signal Strength (RSS) which is not processed to features. Even though it provides adequate results in some cases, but for large-scale environment, it brings the storage problem and computational complexity due to the high dimensionality. To address these problems, this paper presents a feature extraction algorithm using a manifold learning called T-distributed Stochastic Neighbor Embedding (TSNE) which extracts these non-linear fingerprint features and reduces the dimensionality simultaneously at offline stage. Then to increase positioning accuracy, out-of-sample extension method is proposed to process the online record to achieve the same dimensionality as the reduced offline database. Furthermore, when facing the major bottleneck of dimensionality reduction (DR) technologies that determining the proper value of dimensionality, we utilize intrinsic dimensionality estimation method to obtain the best dimensionality previously. Experiments are conducted in an actual indoor large-scale environment, and the results demonstrate our approach performs perfectly which reduces the original dimensionality 168 to 10 and achieves better position accuracy simultaneously.","PeriodicalId":108635,"journal":{"name":"2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Wireless Communications and Signal Processing (WCSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCSP.2019.8928088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Wireless Local Area Network (WLAN) fingerprinting has been extensively studied for indoor localization due to the pervasive facilities. Conventional fingerprint database is composed of a set of raw Received Signal Strength (RSS) which is not processed to features. Even though it provides adequate results in some cases, but for large-scale environment, it brings the storage problem and computational complexity due to the high dimensionality. To address these problems, this paper presents a feature extraction algorithm using a manifold learning called T-distributed Stochastic Neighbor Embedding (TSNE) which extracts these non-linear fingerprint features and reduces the dimensionality simultaneously at offline stage. Then to increase positioning accuracy, out-of-sample extension method is proposed to process the online record to achieve the same dimensionality as the reduced offline database. Furthermore, when facing the major bottleneck of dimensionality reduction (DR) technologies that determining the proper value of dimensionality, we utilize intrinsic dimensionality estimation method to obtain the best dimensionality previously. Experiments are conducted in an actual indoor large-scale environment, and the results demonstrate our approach performs perfectly which reduces the original dimensionality 168 to 10 and achieves better position accuracy simultaneously.
基于流形学习的射频指纹特征提取
由于无线局域网(WLAN)的普及,指纹识别技术在室内定位方面得到了广泛的研究。传统的指纹数据库是由一组未经特征处理的原始接收信号强度(RSS)组成的。尽管它在某些情况下提供了足够的结果,但对于大规模环境,由于高维,它带来了存储问题和计算复杂性。为了解决这些问题,本文提出了一种基于流形学习的特征提取算法,称为t分布随机邻居嵌入(TSNE),该算法在离线阶段提取这些非线性指纹特征并同时降维。然后,为了提高定位精度,提出了样本外扩展方法对在线记录进行处理,使其达到与降维后的离线数据库相同的维数。此外,针对降维技术的主要瓶颈在于确定合适的维数值,我们利用原有的固有维数估计方法来获得最佳维数。在实际的室内大尺度环境中进行了实验,实验结果表明,该方法在将原来的168维数降至10维的同时,取得了较好的定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信