Article(id=1309640048174268586, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250339, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1767888000000, revisedDateStr=2026-01-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172119420, onlineDateStr=2026-09-23, pubDate=1782230400000, pubDateStr=2026-06-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172119420, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172119420, creator=13701087609, updateTime=1790172119420, updator=13701087609, issue=Issue{id=1309640041656316677, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='6', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1782230400000', pubDateStr='2026-06-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172117866, creator='13701087609', updateTime=1790232797347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894549917495577, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894549917495578, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=30, endPage=37, ext={EN=ArticleExt(id=1309640048383983787, articleId=1309640048174268586, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Ultra-Wideband Digital Key Localization System Based on Convolutional Neural Network, columnId=1309640043162075259, journalTitle=Automobile Technology, columnName=Selected Academic Papers of the 28th CAST Annual Meeting, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problem of localization error caused by Non-Line-Of-Sight (NLOS) in the digital key scenario of smart cars, a supervised learning-based classification model is constructed by analyzing the time-domain feature differences of the Channel Impact Response (CIR) dataset to identify the NLOS signals using a Convolutional Neural Network (CNN). Moreover the structure of the CNN network is optimized by considering the limitations of the vehicle-side hardware resources, and the effectiveness and robustness of the Ultra-Wideband (UWB) localization system are verified through real vehicle tests. The experimental results show that the accuracy of the UWB localization system incorporating deep learning and multi-modal localization algorithms is significantly improved compared with the localization trajectory without NLOS recognition processing.

, authors=Zhouxiang Ye¹, Xinyang Wu¹, Qiang Zhang², Hongyin Shan², Yun Chen^{1, 3}, authorsList=Zhouxiang Ye, Xinyang Wu, Qiang Zhang, Hongyin Shan, Yun Chen, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309640048606281900, articleId=1309640048174268586, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于卷积神经网络的数字钥匙超宽带定位系统, columnId=1309640043359207549, journalTitle=汽车技术, columnName=第二十八届中国科协年会学术论文选登, runingTitle=null, highlight=null, articleAbstract=

针对智能汽车数字钥匙场景中非视距（NLOS）导致的定位误差问题，通过分析信道冲击响应（CIR）数据集的时域特征差异，基于监督学习的分类模型使用卷积神经网络（CNN）识别NLOS信号，考虑车端硬件资源限制对CNN网络结构进行了优化，经实车验证UWB定位系统的有效性和鲁棒性。试验结果表明：相比未进行NLOS识别处理的定位轨迹，融合深度学习与多模态定位算法的UWB定位系统的准确度明显提高。

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