NLOS Identification for UWB Based on Channel Impulse Response

Zhuoqi Zeng, Steven Liu, Lei Wang
{"title":"NLOS Identification for UWB Based on Channel Impulse Response","authors":"Zhuoqi Zeng, Steven Liu, Lei Wang","doi":"10.1109/ICSPCS.2018.8631718","DOIUrl":null,"url":null,"abstract":"The localization accuracy of ultra-wide band (UWB) system could be dramatically degraded, if the signal is propagated under non-line-of-sight (NLOS) condition. The detection of the NLOS propagation is very important to guarantee the accuracy of the UWB system. Based on the channel impulse response (CIR) sample, the NLOS condition could be identified. However, for the decawave chips, each CIR sample contains 1015 points. Thus the real-time realization of the NLOS detection with CIR is very hard, since the import and calculation of such a large amount of data cause to huge delay. In order to reduce the delay, the minimal needed size of the points in CIR for accurate NLOS identification is discussed in this paper. The support vector machine (SVM) is used for the classification based on the original CIR points or the eight different features extracted from each CIR. Furthermore, a new method is proposed for the identification based on the convolution algorithm. Compared to the existing approach with CIR, the needed CIR points for the detection are dramatically reduced, which makes the on-line identification realization possible. The accuracy of the NLOS identification with less CIR points is even better. The new proposed method using convolution algorithm also shows very promising results compared the other approaches.","PeriodicalId":179948,"journal":{"name":"2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSPCS.2018.8631718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

Abstract

The localization accuracy of ultra-wide band (UWB) system could be dramatically degraded, if the signal is propagated under non-line-of-sight (NLOS) condition. The detection of the NLOS propagation is very important to guarantee the accuracy of the UWB system. Based on the channel impulse response (CIR) sample, the NLOS condition could be identified. However, for the decawave chips, each CIR sample contains 1015 points. Thus the real-time realization of the NLOS detection with CIR is very hard, since the import and calculation of such a large amount of data cause to huge delay. In order to reduce the delay, the minimal needed size of the points in CIR for accurate NLOS identification is discussed in this paper. The support vector machine (SVM) is used for the classification based on the original CIR points or the eight different features extracted from each CIR. Furthermore, a new method is proposed for the identification based on the convolution algorithm. Compared to the existing approach with CIR, the needed CIR points for the detection are dramatically reduced, which makes the on-line identification realization possible. The accuracy of the NLOS identification with less CIR points is even better. The new proposed method using convolution algorithm also shows very promising results compared the other approaches.
基于信道脉冲响应的超宽带非目标值辨识
超宽带(UWB)系统的定位精度在非视距(NLOS)条件下传播会显著降低。NLOS传播的检测对于保证超宽带系统的精度是非常重要的。基于信道脉冲响应(CIR)样本,可以识别出NLOS状态。而对于十波芯片,每个CIR样本包含1015个点。因此用CIR实时实现NLOS检测是非常困难的,因为如此大量数据的导入和计算会造成巨大的延迟。为了减少延迟,本文讨论了精确识别NLOS所需的最小CIR点的大小。利用支持向量机(SVM)对原始CIR点或从每个CIR中提取的8个不同特征进行分类,并提出了一种基于卷积算法的识别方法。与现有的CIR方法相比,大大减少了检测所需的CIR点,使在线识别成为可能。当CIR点较少时,NLOS识别的准确率更高。与其他方法相比,采用卷积算法的新方法也显示出很好的效果。
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