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Article(id=1244336186618135547, tenantId=1146029695717560320, journalId=1244323073571209252, issueId=1244336186114819067, articleNumber=null, orderNo=null, doi=10.13695/j.cnki.12-1222/o3.2025.10.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1728489600000, receivedDateStr=2024-10-10, revisedDate=null, revisedDateStr=null, acceptedDate=1754150400000, acceptedDateStr=2025-08-03, onlineDate=1774602465538, onlineDateStr=2026-03-27, pubDate=1761753600000, pubDateStr=2025-10-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1774602465538, onlineIssueDateStr=2026-03-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774602465538, creator=13701087609, updateTime=1774602465538, updator=13701087609, issue=Issue{id=1244336186114819067, tenantId=1146029695717560320, journalId=1244323073571209252, year='2025', volume='33', issue='10', pageStart='955', pageEnd='1060', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1774602465418, creator=13701087609, updateTime=1774604459075, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1244344548185452773, tenantId=1146029695717560320, journalId=1244323073571209252, issueId=1244336186114819067, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1244344548185452774, tenantId=1146029695717560320, journalId=1244323073571209252, issueId=1244336186114819067, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=972, endPage=978, ext={EN=ArticleExt(id=1244336188153250814, articleId=1244336186618135547, tenantId=1146029695717560320, journalId=1244323073571209252, language=EN, title=NLOS recognition and localization algorithm based on credibility, columnId=1244336188069364733, journalTitle=Journal of Chinese Inertial Technology, columnName=Integrated Navigation Technology, runingTitle=null, highlight=null, articleAbstract=

To improve the accuracy of Ultra Wide Band (UWB) localization in Non-Line-of-Sight (NLOS) scenarios, a NLOS recognition and localization algorithm based on credibility is proposed. This algorithm utilizes UWB real-time Channel Impulse Response (CIR) features and ranging values to identify Line-of-Sight (LOS) or NLOS through one-dimensional convolution neural network, and outputs the probability of LOS or NLOS. Then this probability is used to construct credibility. Based on credibility, base station selection and improved positioning algorithms are carried out. Weighted Least Squares and Taylor (WLS-Taylor) fusion algorithm based on credibility is designed. Static and dynamic measured data in various scenarios is collected to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively suppress the influence of NLOS on positioning results, and the average positioning error is less than 10 cm in NLOS environment. In environments with relatively severe NLOS, the positioning error of the proposed algorithm is reduced by 76.94 cm compared to the WLS algorithm based on distance weighting.

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为提高非视距场景下超宽带（UWB）定位精度，提出了基于可信度的非视距识别与定位算法。首先，利用UWB诊断寄存器提取实时信道冲击响应特征及测距值，通过一维卷积神经网络进行非视距识别，估计测距为视距或非视距的概率。然后，利用该概率构建可信度，基于可信度进行定位基站筛选及定位算法改进，设计基于可信度的加权最小二乘-泰勒（WLS-Taylor）融合滤波算法。在多种场景下采集静态和动态测试数据进行性能验证，实验结果表明：所提算法能够有效抑制非视距对定位结果的影响，非视距环境下定位误差均值小于10 cm；在非视距相对严重环境下，所提算法的定位误差较基于距离加权的WLS算法降低了76.94 cm。

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刘林（1974—），女，博士，副教授，硕士生导师，从事无线定位研究。

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刘林（1974—），女，博士，副教授，硕士生导师，从事无线定位研究。

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刘林（1974—），女，博士，副教授，硕士生导师，从事无线定位研究。

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Journal of Chinese Inertial Technology, 2023, 31(05): 462-471., articleTitle=Fusion positioning method with UWB/IMU/Odometer based on the improved UKF, refAbstract=null)], funds=[Fund(id=1244336211951731313, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, awardId=SKLKZ19-03, language=CN, fundingSource=轨道交通工程信息化国家重点实验室开放课题(SKLKZ19-03), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1244336200077656368, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, xref=1., ext=[AuthorCompanyExt(id=1244336200136376627, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, companyId=1244336200077656368, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.State Key Laboratory of Intelligent Construction and Maintenance for Geotechnical and Tunnel Engineering under Extreme Environments (FSDI), Xi'an 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caption=Structure diagram of credibility calculation model, figureFileSmall=metC6vMo8Qj25qdpgxZQ1Q==, figureFileBig=sH14TFQo/1B/JE6AN1AbbA==, tableContent=null), ArticleFig(id=1244336206599799247, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图2, caption=可信度计算模型结构示意图, figureFileSmall=metC6vMo8Qj25qdpgxZQ1Q==, figureFileBig=sH14TFQo/1B/JE6AN1AbbA==, tableContent=null), ArticleFig(id=1244336206738211287, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.3, caption=Position calculation algorithm based on credibility, figureFileSmall=WmnLYTYUW+rg15E/yya6BQ==, figureFileBig=popSwclkNeY3pAdTJJJlpg==, tableContent=null), ArticleFig(id=1244336206851457501, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图3, caption=基于可信度的位置解算算法, figureFileSmall=WmnLYTYUW+rg15E/yya6BQ==, figureFileBig=popSwclkNeY3pAdTJJJlpg==, tableContent=null), ArticleFig(id=1244336206935343588, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.4, caption=Positioning measurement site, figureFileSmall=yg4ejEJf5KdIst6K5eBqUg==, figureFileBig=OADfeBrJk8czOojBCbFeJA==, tableContent=null), ArticleFig(id=1244336207019229671, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图4, caption=定位实测场地, figureFileSmall=yg4ejEJf5KdIst6K5eBqUg==, figureFileBig=OADfeBrJk8czOojBCbFeJA==, tableContent=null), ArticleFig(id=1244336207107310061, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.5, caption=CDF of LOS static positioning error, figureFileSmall=kJ7NN1GcGXFwBlXjGywG1w==, figureFileBig=z8VCtTPj+Q3Mcqx4dc2wnA==, tableContent=null), ArticleFig(id=1244336207220556273, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图5, caption=LOS静态定位误差累积分布函数, figureFileSmall=kJ7NN1GcGXFwBlXjGywG1w==, figureFileBig=z8VCtTPj+Q3Mcqx4dc2wnA==, tableContent=null), ArticleFig(id=1244336207304442358, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.6, caption=Positioning scatter plot, figureFileSmall=C5Aph9tUpUEoXn18ABkIyA==, figureFileBig=OXgQkCBFsLi/NWe7Uo04UQ==, tableContent=null), ArticleFig(id=1244336207421882877, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图6, caption=定位散点图, figureFileSmall=C5Aph9tUpUEoXn18ABkIyA==, figureFileBig=OXgQkCBFsLi/NWe7Uo04UQ==, tableContent=null), ArticleFig(id=1244336207627403780, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.7, caption=CDF of NLOS scenario A, figureFileSmall=Kos3HjB4uKwIibFjHL84ZA==, figureFileBig=YpOx0bxO0EVNfS0c79125A==, tableContent=null), ArticleFig(id=1244336207765815817, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图7, caption=NLOS场景A的CDF, figureFileSmall=Kos3HjB4uKwIibFjHL84ZA==, figureFileBig=YpOx0bxO0EVNfS0c79125A==, tableContent=null), ArticleFig(id=1244336207874867726, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.8, caption=CDF of NLOS scenario B, figureFileSmall=9q3OfaaxA65HpnM8MmwxHg==, figureFileBig=9553lSMNOcaBnAbQY+glnA==, tableContent=null), ArticleFig(id=1244336208030056978, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图8, caption=NLOS场景B的CDF, figureFileSmall=9q3OfaaxA65HpnM8MmwxHg==, figureFileBig=9553lSMNOcaBnAbQY+glnA==, tableContent=null), ArticleFig(id=1244336208164274710, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.9, caption=LOS dynamic positioning trajectories, figureFileSmall=CufL2oh/bcxX0P9w8na1ZA==, figureFileBig=OhOPvwQevE9ftZYDgm3VUA==, tableContent=null), ArticleFig(id=1244336208327852574, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图9, caption=LOS动态定位轨迹, figureFileSmall=CufL2oh/bcxX0P9w8na1ZA==, figureFileBig=OhOPvwQevE9ftZYDgm3VUA==, tableContent=null), ArticleFig(id=1244336208449487393, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Fig.10, caption=NLOS dynamic positioning trajectories, figureFileSmall=r9NRmVzFTMCxNJ7T9dJb4A==, figureFileBig=i+Phb0JeL4SZ+Y0v1ru6zQ==, tableContent=null), ArticleFig(id=1244336208520790565, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=图10, caption=NLOS动态定位轨迹, figureFileSmall=r9NRmVzFTMCxNJ7T9dJb4A==, figureFileBig=i+Phb0JeL4SZ+Y0v1ru6zQ==, tableContent=null), ArticleFig(id=1244336208613065257, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.1, caption=

Real time channel characteristics

, figureFileSmall=null, figureFileBig=null, tableContent=
名称说明
dreal实际测距值
F1第一路径（point 1）的幅值
F2第二路径（point 2）的幅值
F3第三路径（point 3）的幅值
Np前导码累积计数长度
Max NoiseDW1000寄存器中的噪声最大值
Std NoiseDW1000寄存器中的噪声标准差
CDW1000寄存器中的CIR功率值
First Path第一路径索引值
FPL第一路径信号功率
RPL接收信号功率
Rate第一路径信号功率与接收信号功率之间的比值
Diff第一路径信号功率与接收信号功率之间的差值
), ArticleFig(id=1244336208684368429, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表1, caption=

信道实时特征

, figureFileSmall=null, figureFileBig=null, tableContent=
名称说明
dreal实际测距值
F1第一路径（point 1）的幅值
F2第二路径（point 2）的幅值
F3第三路径（point 3）的幅值
Np前导码累积计数长度
Max NoiseDW1000寄存器中的噪声最大值
Std NoiseDW1000寄存器中的噪声标准差
CDW1000寄存器中的CIR功率值
First Path第一路径索引值
FPL第一路径信号功率
RPL接收信号功率
Rate第一路径信号功率与接收信号功率之间的比值
Diff第一路径信号功率与接收信号功率之间的差值
), ArticleFig(id=1244336208768254511, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.2, caption=

Hyperparameters of credibility calculation model

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超参数名称数值
学习率学习率衰减（lr=0.001）
优化器Nadam
激活函数Relu
丢弃率0.5
批量训练数据大小64
损失函数sparse_categorical_crossentropy
), ArticleFig(id=1244336208889889332, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表2, caption=

可信度计算模型超参数

, figureFileSmall=null, figureFileBig=null, tableContent=
超参数名称数值
学习率学习率衰减（lr=0.001）
优化器Nadam
激活函数Relu
丢弃率0.5
批量训练数据大小64
损失函数sparse_categorical_crossentropy
), ArticleFig(id=1244336208965386805, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.3, caption=

NLOS identification performance analysis of different models

, figureFileSmall=null, figureFileBig=null, tableContent=
模型精确度召回率F1分数准确率
SVM模型0.910.950.930.93
MLP模型0.950.990.970.97
可信度计算模型0.970.990.980.98
), ArticleFig(id=1244336209036689977, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表3, caption=

不同模型NLOS识别性能分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型精确度召回率F1分数准确率
SVM模型0.910.950.930.93
MLP模型0.950.990.970.97
可信度计算模型0.970.990.980.98
), ArticleFig(id=1244336210563416638, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.4, caption=

LOS static positioning error (Unit: cm)

, figureFileSmall=null, figureFileBig=null, tableContent=
算法均值标准差RMSE
基于距离加权的WLS2.760.802.88
基于可信度加权的WLS2.500.932.71
本文算法2.320.342.35
), ArticleFig(id=1244336210659885632, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表4, caption=

LOS静态定位误差（单位：厘米）

, figureFileSmall=null, figureFileBig=null, tableContent=
算法均值标准差RMSE
基于距离加权的WLS2.760.802.88
基于可信度加权的WLS2.500.932.71
本文算法2.320.342.35
), ArticleFig(id=1244336210777326148, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.5, caption=

Corresponding error of typical CDF for LOS static positioning (Unit: cm)

, figureFileSmall=null, figureFileBig=null, tableContent=
算法CDF
60%75%90%
基于距离加权的WLS2.873.203.83
基于可信度加权的WLS2.703.003.79
本文算法2.352.492.70
), ArticleFig(id=1244336210877989449, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表5, caption=

LOS静态定位典型CDF值对应误差（单位：厘米）

, figureFileSmall=null, figureFileBig=null, tableContent=
算法CDF
60%75%90%
基于距离加权的WLS2.873.203.83
基于可信度加权的WLS2.703.003.79
本文算法2.352.492.70
), ArticleFig(id=1244336210991235660, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.6, caption=

Positioning error of NLOS scenario A (Unit: cm)

, figureFileSmall=null, figureFileBig=null, tableContent=
算法均值标准差RMSE
基于距离加权的WLS7.8810.5513.17
基于可信度加权的WLS5.114.296.68
本文算法4.712.155.18
), ArticleFig(id=1244336211091898960, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表6, caption=

NLOS场景A的定位误差（单位：厘米）

, figureFileSmall=null, figureFileBig=null, tableContent=
算法均值标准差RMSE
基于距离加权的WLS7.8810.5513.17
基于可信度加权的WLS5.114.296.68
本文算法4.712.155.18
), ArticleFig(id=1244336211238699607, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.7, caption=

Corresponding error of typical CDF for NLOS scenario A (Unit: cm)

, figureFileSmall=null, figureFileBig=null, tableContent=
算法CDF
60%75%90%
基于距离加权的WLS6.069.3013.78
基于可信度加权的WLS5.036.178.63
本文算法5.656.036.87
), ArticleFig(id=1244336211326779995, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表7, caption=

NLOS场景A典型CDF值对应误差（单位：厘米）

, figureFileSmall=null, figureFileBig=null, tableContent=
算法CDF
60%75%90%
基于距离加权的WLS6.069.3013.78
基于可信度加权的WLS5.036.178.63
本文算法5.656.036.87
), ArticleFig(id=1244336211419054691, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.8, caption=

Positioning error of NLOS scenario B (Unit: cm)

, figureFileSmall=null, figureFileBig=null, tableContent=
算法均值标准差RMSE
基于距离加权的WLS33.1533.9647.45
基于可信度加权的WLS14.2228.8132.13
本文算法7.228.1310.87
), ArticleFig(id=1244336211540689510, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表8, caption=

NLOS场景B的定位误差（单位：厘米）

, figureFileSmall=null, figureFileBig=null, tableContent=
算法均值标准差RMSE
基于距离加权的WLS33.1533.9647.45
基于可信度加权的WLS14.2228.8132.13
本文算法7.228.1310.87
), ArticleFig(id=1244336211695878761, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=EN, label=Tab.9, caption=

Corresponding error of typical CDF for NLOS scenario B (Unit: cm)

, figureFileSmall=null, figureFileBig=null, tableContent=
算法CDF
60%75%90%
基于距离加权的WLS36.6550.8887.59
基于可信度加权的WLS5.846.0553.52
本文算法6.407.1910.65
), ArticleFig(id=1244336211796542061, tenantId=1146029695717560320, journalId=1244323073571209252, articleId=1244336186618135547, language=CN, label=表9, caption=

NLOS场景B典型CDF值对应误差（单位：厘米）

, figureFileSmall=null, figureFileBig=null, tableContent=
算法CDF
60%75%90%
基于距离加权的WLS36.6550.8887.59
基于可信度加权的WLS5.846.0553.52
本文算法6.407.1910.65
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基于可信度的非视距识别与定位算法
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刘林 1, 2 , 宋雨昊 2
中国惯性技术学报 | 组合导航技术 2025,33(10): 972-978
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中国惯性技术学报 | 组合导航技术 2025, 33(10): 972-978
基于可信度的非视距识别与定位算法
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刘林1, 2, 宋雨昊2
作者信息
  • 1.极端环境岩土和隧道工程智能建养全国重点实验室（中铁一院），西安　710043
  • 2.西南交通大学　信息编码与传输省重点实验室，成都　611756

    • 刘林（1974—），女，博士，副教授，硕士生导师，从事无线定位研究。

NLOS recognition and localization algorithm based on credibility
Lin LIU1, 2, Yuhao SONG2
Affiliations
  • 1.State Key Laboratory of Intelligent Construction and Maintenance for Geotechnical and Tunnel Engineering under Extreme Environments (FSDI), Xi'an 710043, China
  • 2.Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 611756, China
出版时间: 2025-10-30 doi: 10.13695/j.cnki.12-1222/o3.2025.10.003
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摘要
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为提高非视距场景下超宽带（UWB）定位精度，提出了基于可信度的非视距识别与定位算法。首先，利用UWB诊断寄存器提取实时信道冲击响应特征及测距值，通过一维卷积神经网络进行非视距识别，估计测距为视距或非视距的概率。然后，利用该概率构建可信度，基于可信度进行定位基站筛选及定位算法改进，设计基于可信度的加权最小二乘-泰勒（WLS-Taylor）融合滤波算法。在多种场景下采集静态和动态测试数据进行性能验证，实验结果表明：所提算法能够有效抑制非视距对定位结果的影响，非视距环境下定位误差均值小于10 cm；在非视距相对严重环境下，所提算法的定位误差较基于距离加权的WLS算法降低了76.94 cm。

超宽带  /  信道响应特征  /  非视距识别  /  一维深度卷积神经网络  /  可信度

To improve the accuracy of Ultra Wide Band (UWB) localization in Non-Line-of-Sight (NLOS) scenarios, a NLOS recognition and localization algorithm based on credibility is proposed. This algorithm utilizes UWB real-time Channel Impulse Response (CIR) features and ranging values to identify Line-of-Sight (LOS) or NLOS through one-dimensional convolution neural network, and outputs the probability of LOS or NLOS. Then this probability is used to construct credibility. Based on credibility, base station selection and improved positioning algorithms are carried out. Weighted Least Squares and Taylor (WLS-Taylor) fusion algorithm based on credibility is designed. Static and dynamic measured data in various scenarios is collected to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively suppress the influence of NLOS on positioning results, and the average positioning error is less than 10 cm in NLOS environment. In environments with relatively severe NLOS, the positioning error of the proposed algorithm is reduced by 76.94 cm compared to the WLS algorithm based on distance weighting.

UWB  /  channel impulse response  /  NLOS recognition  /  one-dimensional depth convolution neural network  /  credibility
刘林, 宋雨昊. 基于可信度的非视距识别与定位算法. 中国惯性技术学报, 2025 , 33 (10) : 972 -978 . DOI: 10.13695/j.cnki.12-1222/o3.2025.10.003
Lin LIU, Yuhao SONG. NLOS recognition and localization algorithm based on credibility[J]. Journal of Chinese Inertial Technology, 2025 , 33 (10) : 972 -978 . DOI: 10.13695/j.cnki.12-1222/o3.2025.10.003
正文
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随着时代的发展，人们对基于位置的服务需求与日俱增[1]。各种室内定位技术中，超宽带（Ultra Wide Band，UWB）具有功耗低、抗多径干扰及穿透能力强和时间分辨率高等优势，在室内定位系统中得到广泛应用[2-4]。UWB定位技术的位置估计误差主要由系统测量误差和非视距（Non-Line-of-Sight，NLOS）误差引起[5-7]。系统测量误差一般很小，对定位精度影响有限；而NLOS误差情况复杂，不易处理，使得UWB定位精度急剧下降。NLOS识别[8-14]以及NLOS抑制[15,16]是目前处理NLOS误差的主流策略。
文献[8]和文献[9]利用两个固定节点和一个移动节点进行测距，通过两个固定节点之间的菲涅耳区域进行移动节点的NLOS识别，这种方法在可变环境中是有效的，且无需大量先验知识。但该算法未讨论标签两侧都有障碍物的情况。文献[10]~文献[14]基于UWB信道冲激响应（Channel Impulse Response，CIR）及接收波形特征（如接收信号最大幅值、上升时间等），利用卷积神经网络（Convolution Neural Network，CNN）、长短期记忆网络（Long Short-term Memory，LSTM）等方法进行NLOS识别，提高了识别准确性。这些方法通过二元分类模型区分LOS（Line of Sight）与NLOS基站，并利用LOS基站进行位置估计以提升定位精度。然而，当基站数量有限且LOS传播受限时，由于LOS基站不足，需依赖部分NLOS基站进行位置估计。现有二分类方法进行LOS和NLOS识别时，对NLOS基站的非视距严重情况并没有进行评估，因此定位过程中无法选择NLOS较轻的基站。此外，UWB测距信号采集过程中的噪声也会影响机器学习算法对NLOS信号的鉴别准确性。
在UWB NLOS抑制方法中，文献[15]提出了一种针对多壁障碍物环境的UWB室内定位NLOS抑制方法，通过建立UWB穿壁测距误差模型生成误差图，结合灰狼优化算法来缓解NLOS误差的影响。文献[16]采用LSTM估计距离误差并进行测距修正，结合距离加权最小二乘算法与卡尔曼滤波抑制NLOS误差，提升了定位性能，但对不稳定的测量噪声适应性不足。此外，由于测距误差等因素，距离参数难以准确反映基站信号质量，影响加权效果。针对这些问题，本文提出基于可信度的NLOS识别与定位算法，通过可信度评估NLOS严重程度，在LOS基站不足时依据可信度筛选基站并实现加权定位。
1　定位方案设计
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随着人工智能的发展，深度学习算法能够自动从庞杂数据中提取特征。UWB诊断寄存器提供的CIR包含丰富的位置相关信息（如距离、功率等，见表1）。因此，基于CIR特征[11,12]结合深度学习的NLOS识别与抑制算法成为研究热点。
本文首先将DW1000 UWB诊断寄存器中的实时CIR特征和测距值作为一维卷积神经网络分类模型的输入，通过该模型进行LOS和NLOS识别，输出LOS和NLOS的概率值，并据此构建测距信息的可信度。然后，根据可信度筛选参与定位的基站，并依据参与定位基站的可信度设计各基站的权重，利用该权重改进传统加权最小二乘（Weighted Least Squares，WLS）定位算法的加权矩阵以及Taylor算法迭代过程中误差协方差矩阵。基于可信度的WLS-Taylor融合滤波算法利用WLS得到的位置粗估计值作为Taylor算法初始值，迭代修正位置估计结果，提高NLOS场景下的定位性能。
本文基于可信度的NLOS识别与定位算法框图如图1所示。
2　基于一维卷积神经网络的可信度计算方法
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为降低NLOS误差对定位精度的影响，本文基于表1数据设计了采用卷积神经网络定量计算可信度的方法。
2.1　可信度的定量计算方法
为了实现NLOS识别并同时表征NLOS的严重程度，本节给出可信度的定义，并设计了可信度的定量计算方法。
设二分类NLOS识别算法输出两个概率值：PLOS表示输入样本属于LOS类别的概率，PNLOS表示输入样本属于NLOS类别的概率。若PNLOS…PLOS，则基站测距值被判为NLOS测距；反之则被判为LOS测距。
令，其值越大表明依据本次分类结果进行NLOS识别的结果越可靠，即PLOS与PNLOS之间的差值越大，分类结果就越可靠。故该差值的大小一定程度上反映了分类的可信度。当PLOS远大于PNLOS时，表示该实际测距值为LOS传播条件下的测距值可能性更高；当PLOS远小于PNLOS时，表示该实际测距值为LOS传播条件下的测距值可能性更小。故本文定义了如式（1）所示的可信度参数ξ。由于PLOS与PNLOS之和为1，ξ的取值范围为闭区间[e-1，e]。
由式（1）可知，当ξ>1时，该基站为LOS传播，且ξ越大，为LOS的概率越大；当ξ≤1时，该基站为NLOS传播，且ξ越小，为NLOS的概率越大。
2.2　可信度计算模型设计
计算可信度ξ的关键是获取PLOS和PNLOS，由文献[10]~文献[14]可知，支持向量机（Support Vector Machines，SVM）、多层感知（Multilayer Perceptron，MLP）、决策树、CNN、LSTM等机器学习方法常用于NLOS/LOS分类。相对其它方法，CNN更容易通过学习CIR的各种特征实现NLOS/LOS分类，因此，本文以一维卷积神经网络分类模型为基础，设计可信度定量计算模型。所构建的可信度计算模型如图2所示。
卷积神经网络由卷积层、池化层、全连接层等交叉堆叠而成。卷积层是整个神经网络中最重要的一层，且该层最核心的部分是卷积核，不同的卷积核相当于不同的特征提取器，用于学习隐含在数据中的复杂特征。在每个卷积层之后，采用池化层降低特征维数，避免过拟合。3个卷积层和1个池化层组成一个卷积块，连续堆叠2个卷积块。卷积块后利用一个全连接层对高度抽象的特征进行整合，以获取非线性组合特征。基于全连接层输出特征，利用softmax层进行LOS和NLOS分类识别，输出分类结果的概率值PLOS和PNLOS，由此构建可信度。
为了处理过拟合问题，可信度计算模型中加入了BN（Batch Normalization）层以及Dropout层。图2的可信度计算模型超参数设置如表2所示。
2.3　可信度计算结果与讨论
为评估图2所示可信度计算模型的性能，本文采用式（2）进行LOS和NLOS判决，并通过精确度、召回率、F1分数和准确率衡量其性能。
精确度（Precision）表示模型将正类样本预测为正类的数量与将所有样本预测为正类的数量之比，如式（3）所示。
召回率（Recall）表示模型正类样本正确分类的数量与正类样本数量的比值，如式（4）所示。
F1分数（F1-Score）用来综合衡量模型的召回率和精确度，如式（5）所示。其中，当α=1时，即为F1-Score。
准确率（Accuracy）用来表示预测正确的结果占总数据集的百分比，如式（6）所示。
式（3）~式（6）中，TP是真阳性，FP是假阳性，FN是假阴性，TN是真阴性。TP表示实际为正时被归类为正，TN表示实际为负时被归类为负，FP表示当实际为负时被分类为正。类似地，FN表示在实际为正时被分类为负。
本节将可信度计算模型与SVM模型、MLP模型[14]进行对比。其中，SVM模型参数设置为：SVC（C=1.0，decision_ function_shape='ovr'，kernel='rbf'，tol=0.001）。MLP模型参数设置为：MLPClassifier（activation='relu'，hidden_layer_sizes=（100，100），learning_rate_init=0.001，solver='adam'）。
数据集样本数量共为39200个，其中训练集和验证集占80%（31360个），测试集占20%（7840个）。如表3所示，三种模型中，可信度计算模型精确度比SVM模型和MLP模型分别高0.06、0.02；F1分数和准确率比SVM模型和MLP模型分别高0.05、0.01；召回率与MLP模型相当，但高于SVM模型。总体来看，可信度计算模型是三种模型中性能最优的。
3　基于可信度的位置解算算法
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NLOS误差是影响UWB定位精度的主要因素。当测距误差较大的基站参与定位时，会导致定位精度显著下降。由前面的分析可知，可信度ξ越大，LOS传播概率越大；ξ越小，NLOS传播概率越大；且ξ>1时为LOS传播，ξ≤1时为NLOS传播。因此，可依据ξ>1的基站数量判断视距基站能否完成定位。如果不行，则可选择ξ值比较大的非视距基站参与定位。
此外，ξ值的大小一定程度上也反映了测距误差大小。ξ越大，测距误差越小；相反，测距误差越大。因此，在进行定位解算过程中，ξ越大的基站其权重应该越大。
基于以上分析，本文提出的基于可信度的位置解算算法首先根据可信度筛选基站，选用可信度较高的基站参与位置解算，并基于可信度设计WLS-Taylor融合滤波算法，具体实现流程如图3所示。
3.1　基于可信度的基站筛选
根据可信度计算结果，视各基站可信度值的不同分布情况进行基站选择。具体方法如下：
第一步：基站与标签间进行通信，获取各基站测距值及相对应的实时CIR信息。
第二步：基于可信度计算模型得到各基站的可信度。
第三步：按照可信度从大到小的顺序对各基站进行排序。
第四步：判断所有基站的可信度是否均不大于1。若符合该条件，说明所有基站均为NLOS基站的可能性较大，此时根据实际测距值进行排序，选择最近的四个基站进行后续定位解算。若不满足判断条件，则执行第五步。
第五步：判断可信度ξ>1的基站个数是否小于4。若不小于4，则筛选出可信度ξ>1的所有基站；若小于4，则选择排序后可信度较大的四个基站。该条件的设定是为了使NLOS基站尽可能不参与后面的定位解算。
3.2　基于可信度的WLS-Taylor融合滤波算法
假设筛选出的N个参与定位的基站位置坐标分别为（x1，y1），（x2，y2），…，（xN，yN），标签的位置坐标为（x，y），基站到标签的距离分别为d1，d2，…，dN。
根据基站与标签间的测距信息建立二元二次方程组：
该方程组可以转化为矩阵形式：
其中，
式（9）中，，r=x2+y2。
采用加权最小二乘算法计算标签的位置坐标：
其中，加权矩阵。
若N个基站的可信度分别为ξ1，ξ2，…，ξN，由前面的分析可知，ξ越大的基站权重应该越大，即式（10）中加权矩阵W的对应值应该越大。为此，本文设计的加权方式如式（11）所示。
由式（11）可知，当ξi越大时，Wii越大。将式（11）代入式（10），即可得到标签位置（x，y）和变量r。标签位置（x，y）和变量r之间存在约束关系r=x2+y2，但是式（10）的求解忽略了该约束关系，因此估计位置通常还存在较大误差。非视距导致的测距误差（噪声）通常较大且不一定服从高斯分布，WLS虽通过降低高噪声数据点的权重抑制噪声影响，但仍存在残余误差，而迭代求解在处理噪声较大的数据时具有强大的能力。为此，本文提出了基于可信度的WLS-Taylor融合滤波定位算法。
记由式（10）得到的标签位置为（xWLS，yWLS），将其作为Taylor算法的初始估计位置（xˆ，yˆ）。同时，利用可信度构建到达时间（Time of Arrival，TOA）误差协方差矩阵Q，依据Taylor算法进行迭代求解，即可实现基于可信度的WLS-Taylor融合滤波算法。具体过程如下：
第一步：令Taylor算法的初始值为。
第二步：对在初始位置处用Taylor级数展开，并忽略二次以上的展开项，结果如式（12）所示。
其中，∆x、∆y分别为x、y误差，εi为测距误差。
整理可得：
其中，。
将方程组（13）转化为矩阵形式：
其中，
令φ=0，即可得到真实坐标与估计坐标间的误差δ为：
其中，矩阵Q中的元素
更新标签位置坐标：
第三步：判断阈值条件是否小于门限值。如果满足则停止迭代，执行第四步。不满足则令，回溯到第二步继续迭代，直至满足阈值条件。
第四步：输出定位结果。
为进一步降低定位结果中的误差，采用平方根无迹卡尔曼滤波算法[17-19]（Square Root Unscented Kalman Filter，SRUKF）对上述定位结果进行平滑处理。
4　实验结果与分析
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本节通过实验对比分析位置解算过程中加权方式分别为距离加权[16]和可信度加权的WLS算法与本文基于可信度的位置解算算法的性能差异。
实验设置六个基站和一个标签。基站坐标分别为：基站A（0，0）、基站B（600 cm，0）、基站C（600 cm，660 cm）、基站D（0，660 cm）、基站E（0，300 cm）、基站F（600 cm，300 cm）。在该场地设置三个静态定位采集点，坐标分别为（360 cm，300 cm）、（420 cm，420 cm）、（300 cm，240 cm），每个定位点采集1000组数据。实测场地环境如图4所示。
在该场景下，当基站与标签间无遮挡（LOS）时，如表4所示，三种算法的定位误差均值、标准差及均方根误差（Root Mean Square Error，RMSE）数值相近，表明LOS条件下各算法性能相当。
三种算法的定位误差累积分布函数（Cumulative Distribution Function，CDF）如图5所示。当CDF分别为60%、75%和90%时，对应的定位误差见表5。
可以看出，本文算法误差最小，算法性能略优于其他定位算法。LOS静态定位精度达到厘米级，定位误差小于3 cm。
从图5可以发现，本文算法定位误差小于2 cm的概率略低于其他算法。为探究原因，本文随机选取某次实验数据绘制图6所示的定位结果散点图。结果表明，该算法定位结果集中度较高，故定位误差分布也会较集中，但略偏离标记的静态定位点，因此出现上述现象。
在图4所示的定位区域内，通过行人行走（标记为NLOS场景A）或人体长时间遮挡部分基站（标记为NLOS场景B）构建NLOS环境。表6给出NLOS场景A下各算法的定位误差。
图7展示了NLOS场景A下三种算法的定位误差累积分布函数，表7给出CDF为60%、75%和90%时对应的定位误差。
在NLOS场景B下，三种算法的定位误差如表8所示。图8展示了NLOS场景B下三种算法的定位误差累积分布函数，表9给出CDF为60%、75%和90%时对应的定位误差。
从上面的结果可知，当定位误差CDF=90%时，在NLOS环境A下，基于可信度加权的WLS算法和本文算法的定位误差相对于基于距离加权的WLS算法分别降低了5.15 cm、6.91 cm；NLOS环境B下分别降低了34.07 cm、76.94 cm，验证了可信度对算法改进的有效性。在非视距相对严重的环境B下，本文算法由于引入了滤波处理，标准差相对于基于可信度加权的WLS算法和基于距离加权的WLS算法减少了20.68 cm和25.83 cm，表明其滤波机制能有效抑制残留的NLOS误差，使定位结果更加稳定，有效提升了NLOS环境下UWB定位精度。本文算法在两种NLOS测试环境下定位误差均值都小于10 cm。
图9和图10分别展示了图4场地在LOS和NLOS环境下的动态定位结果。实验表明：在LOS场景中，三种算法的定位轨迹均能较好贴合真实轨迹；而在NLOS场景下，基于距离和可信度加权的WLS算法定位性能下降，本文算法明显优于这两种定位算法，其轨迹更贴近真实路径，有效提升了NLOS环境中UWB的定位性能。
5　结论
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本文提出一种基于可信度的NLOS识别与定位算法，采用CIR的一维卷积神经网络计算可信度，并设计可信度加权融合定位算法。多种场景实验验证了基于可信度的NLOS识别与定位算法的有效性。实验结果表明，基于可信度的基站筛选能有效识别NLOS基站，基于可信度的WLS-Taylor融合算法可提升UWB在NLOS场景下的定位精度。
基金
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  • 轨道交通工程信息化国家重点实验室开放课题(SKLKZ19-03)
文献
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2025年第33卷第10期
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doi: 10.13695/j.cnki.12-1222/o3.2025.10.003
  • 接收时间：2024-10-10
  • 首发时间：2026-03-27
  • 出版时间：2025-10-30
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  • 收稿日期：2024-10-10
  • 录用日期：2025-08-03
基金
轨道交通工程信息化国家重点实验室开放课题(SKLKZ19-03)
作者信息
    1.极端环境岩土和隧道工程智能建养全国重点实验室（中铁一院），西安　710043
    2.西南交通大学　信息编码与传输省重点实验室，成都　611756
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