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Article(id=1322566764379091541, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322566763112415546, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.02.037, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1734883200000, receivedDateStr=2024-12-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793254088730, onlineDateStr=2026-10-29, pubDate=1768406400000, pubDateStr=2026-01-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793254088730, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793254088730, creator=13701087609, updateTime=1793254088730, updator=13701087609, issue=Issue{id=1322566763112415546, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='1', pageStart='1', pageEnd='138', issueExtLink='null', onlineDate='null', pubDate='1768406400000', pubDateStr='2026-01-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793254088427, creator='13701087609', updateTime=1793254088427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=9, endPage=16, ext={EN=ArticleExt(id=1322566764546863702, articleId=1322566764379091541, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=NLOS Signal Identification and PPP Optimization Method for Low-Cost Device, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In urban environments, range measurement errors caused by non-line-of-sight (NLOS) signals significantly impact the precise positioning performance of global navigation satellite systems (GNSS), especially for low-cost devices and precise point positioning (PPP) that require accurate error processing. To address this issue, this paper designs a method for detecting NLOS signals based on the inertial navigation system (INS) and fisheye camera, and proposes an equivalent weight matrix update scheme for NLOS signals based on observation residuals to mitigate the adverse effects of NLOS signals on PPP. In the experiments, low-cost helical antennas and u-blox boards were used to collect two sets of vehicle-mounted data in Wuhan and Zhengzhou. The results show that the proportion of NLOS signal occurrences varies significantly across different scenarios, with tree shading and building blockage being the main factors causing NLOS signals. The optimized PPP achieves significant improvements in positioning accuracy and stability, especially in bridge-shaded and tree-shaded environments. The positioning errors were reduced from 21.259 m and 11.814 m to 17.071 m and 9.239 m, respectively, with improvement rates of 19.7% and 21.8%.

, authors=Linyang LI1, Jiawei YOU1, Yang CUI2, Qingyong ZHOU3, Zhenbang XU1, authorsList=Linyang LI, Jiawei YOU, Yang CUI, Qingyong ZHOU, Zhenbang XU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., 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=1322566769009603183, articleId=1322566764379091541, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=低成本设备NLOS信号识别和PPP优化方法, columnId=1281909276545356011, journalTitle=大地测量与地球动力学, columnName=大地测量学, runingTitle=null, highlight=null, articleAbstract=

在城市环境中，非视距(NLOS)信号所导致的测距误差对全球导航卫星系统(GNSS)的精密定位性能产生了显著影响，特别是对于需要精确处理误差的低成本设备和精密单点定位(PPP)。为此，设计了一种基于惯性导航系统(INS)和鱼眼相机的NLOS信号检测方法，并提出一种基于观测值残差的NLOS信号等价权矩阵更新方案，以削弱NLOS信号对PPP的不利影响。实验采用低成本螺旋天线和u-blox板卡，在武汉和郑州分别采集了2组车载数据。结果表明，在不同场景下，NLOS信号的发生比例存在较大差异，其中林荫遮挡和建筑物遮挡是导致NLOS信号出现的主要因素。经过优化的PPP在定位精度和稳定性方面均得到显著提升，特别是在桥梁遮挡和林荫环境下，提升效果最为明显，定位误差分别从21.259 m和11.814 m降低至17.071 m和9.239 m, 提升幅度分别达到19.7%和21.8%。

, authors=李林阳1, 游嘉伟1, 崔阳2, 周庆勇3, 徐振邦1, authorsList=李林阳, 游嘉伟, 崔阳, 周庆勇, 徐振邦, authorCompany=null, correspAuthors=null, authorNote=

李林阳, 博士, 副教授, 主要研究方向为导航定位与位置服务, E-mail:

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李林阳, 博士, 副教授, 主要研究方向为导航定位与位置服务, E-mail:

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李林阳, 博士, 副教授, 主要研究方向为导航定位与位置服务, E-mail:

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Experimental scene classification and number of images

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场地编号采集数据场景天空图像数
武汉市01单边遮挡(树木或建筑物)345
02城市峡谷(两侧连续规则建筑物遮挡)435
03单边建筑和单边树木遮挡326
04双边建筑和单边树木遮挡606
05连续过桥(在高架桥下长时间行驶)451
06桥梁遮挡(高架桥或人行天桥等)141
07双边林荫遮盖(两侧和顶部树木遮挡)239
08双边林荫遮盖和建筑遮挡(两侧和顶部树木+外层建筑遮挡)177
09开阔环境387
郑州市01单边遮挡(树木或建筑物)102
03单边建筑和单边树木遮挡128
04双边建筑和单边树木遮挡193
07双边林荫遮盖(两侧和顶部树木遮挡)279
08双边林荫遮盖和建筑遮挡(两侧和顶部树木+外层建筑遮挡)104
总计3 913
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实验场景分类和图像数

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场地编号采集数据场景天空图像数
武汉市01单边遮挡(树木或建筑物)345
02城市峡谷(两侧连续规则建筑物遮挡)435
03单边建筑和单边树木遮挡326
04双边建筑和单边树木遮挡606
05连续过桥(在高架桥下长时间行驶)451
06桥梁遮挡(高架桥或人行天桥等)141
07双边林荫遮盖(两侧和顶部树木遮挡)239
08双边林荫遮盖和建筑遮挡(两侧和顶部树木+外层建筑遮挡)177
09开阔环境387
郑州市01单边遮挡(树木或建筑物)102
03单边建筑和单边树木遮挡128
04双边建筑和单边树木遮挡193
07双边林荫遮盖(两侧和顶部树木遮挡)279
08双边林荫遮盖和建筑遮挡(两侧和顶部树木+外层建筑遮挡)104
总计3 913
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Statistics of NLOS signal identification results

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场景所有历元卫星总数LOS信号数NLOS信号数NLOS比例/%
01单边遮挡2 1171 79422310.5
02城市峡谷5 8694 78688315.0
03单边建筑和单边树木遮挡4 0712 90669817.1
04双边建筑和单边树木遮挡9 4546 7802 47426.2
05连续过桥4 8324 0796531.5
06桥梁遮挡6 8255 9363945.8
07双边林荫遮盖3 2822 5313119.4
08双边林荫遮盖和建筑遮挡2 2359421 1935.4
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NLOS信号识别结果统计

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场景所有历元卫星总数LOS信号数NLOS信号数NLOS比例/%
01单边遮挡2 1171 79422310.5
02城市峡谷5 8694 78688315.0
03单边建筑和单边树木遮挡4 0712 90669817.1
04双边建筑和单边树木遮挡9 4546 7802 47426.2
05连续过桥4 8324 0796531.5
06桥梁遮挡6 8255 9363945.8
07双边林荫遮盖3 2822 5313119.4
08双边林荫遮盖和建筑遮挡2 2359421 1935.4
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Multi-system PPP processing strategies

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类型处理策略
卫星频率GPS：L1+L2、GALILEO、E1+E5a、BDS：B1I+B2I GLONASS：G1+G2、QZSS：L1+L2
采样间隔1 s
截止高度角7°
随机模型优化前：联合信噪比和高度角定权优化后：联合信噪比和高度角定权、IGGⅢ模型
精密星历武汉大学发布的多系统精密产品
电离层延迟作为待估参数进行估计
对流层延迟先验：Saastamoinen模型+GPT3气象参数；映射函数：VMF；残余对流层延迟作为待估参数估计
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多系统PPP处理策略

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类型处理策略
卫星频率GPS：L1+L2、GALILEO、E1+E5a、BDS：B1I+B2I GLONASS：G1+G2、QZSS：L1+L2
采样间隔1 s
截止高度角7°
随机模型优化前：联合信噪比和高度角定权优化后：联合信噪比和高度角定权、IGGⅢ模型
精密星历武汉大学发布的多系统精密产品
电离层延迟作为待估参数进行估计
对流层延迟先验：Saastamoinen模型+GPT3气象参数；映射函数：VMF；残余对流层延迟作为待估参数估计
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Statistics of PPP error before and after optimization

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场景定位解类型定位分量RMS/m三维RMS/m精度提升
01单边遮挡优化前水平4.5288.910-
垂直7.674
优化后水平4.1207.41716.7%
垂直6.167
03单边建筑和单边树木遮挡+04双边建筑和单边树木遮挡优化前水平6.0238.370-
垂直5.812
优化后水平5.0597.37711.8%
垂直5.639
06桥梁遮挡优化前水平9.50121.259-
垂直19.018
优化后水平9.00817.07119.7%
垂直14.501
07双边林荫遮盖+08双边林荫遮盖和建筑遮挡优化前水平4.33611.814-
垂直10.990
优化后水平4.0129.23921.8%
垂直8.322
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优化前后PPP误差统计

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场景定位解类型定位分量RMS/m三维RMS/m精度提升
01单边遮挡优化前水平4.5288.910-
垂直7.674
优化后水平4.1207.41716.7%
垂直6.167
03单边建筑和单边树木遮挡+04双边建筑和单边树木遮挡优化前水平6.0238.370-
垂直5.812
优化后水平5.0597.37711.8%
垂直5.639
06桥梁遮挡优化前水平9.50121.259-
垂直19.018
优化后水平9.00817.07119.7%
垂直14.501
07双边林荫遮盖+08双边林荫遮盖和建筑遮挡优化前水平4.33611.814-
垂直10.990
优化后水平4.0129.23921.8%
垂直8.322
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低成本设备NLOS信号识别和PPP优化方法
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李林阳 1 , 游嘉伟 1 , 崔阳 2 , 周庆勇 3 , 徐振邦 1
大地测量与地球动力学 | 大地测量学 2026,46(1): 9-16
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大地测量与地球动力学 |大地测量学 2026 , 46 (1) : 9 -16
低成本设备NLOS信号识别和PPP优化方法
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李林阳1 , 游嘉伟1, 崔阳2, 周庆勇3, 徐振邦1
作者信息
  • 1 信息工程大学地理空间信息学院, 郑州, 450001
  • 2 联勤保障部队工程大学军事设施系, 重庆, 401331
  • 3 西安测绘研究所, 西安, 710054
作者简介:

李林阳, 博士, 副教授, 主要研究方向为导航定位与位置服务, E-mail:

NLOS Signal Identification and PPP Optimization Method for Low-Cost Device
Linyang LI1 , Jiawei YOU1, Yang CUI2, Qingyong ZHOU3, Zhenbang XU1
Affiliations
  • 1 School of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China
  • 2 Military Facility Departnent, PLA Joint Logistics Support Force University of Engineering, Chongqing 401331, China
  • 3 Xi'an Institute of Surveying and Mapping, Xi'an 710054, China
出版时间: 2026-01-15 doi: 10.14075/j.jgg.2025.02.037
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在城市环境中，非视距(NLOS)信号所导致的测距误差对全球导航卫星系统(GNSS)的精密定位性能产生了显著影响，特别是对于需要精确处理误差的低成本设备和精密单点定位(PPP)。为此，设计了一种基于惯性导航系统(INS)和鱼眼相机的NLOS信号检测方法，并提出一种基于观测值残差的NLOS信号等价权矩阵更新方案，以削弱NLOS信号对PPP的不利影响。实验采用低成本螺旋天线和u-blox板卡，在武汉和郑州分别采集了2组车载数据。结果表明，在不同场景下，NLOS信号的发生比例存在较大差异，其中林荫遮挡和建筑物遮挡是导致NLOS信号出现的主要因素。经过优化的PPP在定位精度和稳定性方面均得到显著提升，特别是在桥梁遮挡和林荫环境下，提升效果最为明显，定位误差分别从21.259 m和11.814 m降低至17.071 m和9.239 m, 提升幅度分别达到19.7%和21.8%。

低成本设备  /  非视距信号  /  惯性导航系统  /  鱼眼相机  /  PPP优化

In urban environments, range measurement errors caused by non-line-of-sight (NLOS) signals significantly impact the precise positioning performance of global navigation satellite systems (GNSS), especially for low-cost devices and precise point positioning (PPP) that require accurate error processing. To address this issue, this paper designs a method for detecting NLOS signals based on the inertial navigation system (INS) and fisheye camera, and proposes an equivalent weight matrix update scheme for NLOS signals based on observation residuals to mitigate the adverse effects of NLOS signals on PPP. In the experiments, low-cost helical antennas and u-blox boards were used to collect two sets of vehicle-mounted data in Wuhan and Zhengzhou. The results show that the proportion of NLOS signal occurrences varies significantly across different scenarios, with tree shading and building blockage being the main factors causing NLOS signals. The optimized PPP achieves significant improvements in positioning accuracy and stability, especially in bridge-shaded and tree-shaded environments. The positioning errors were reduced from 21.259 m and 11.814 m to 17.071 m and 9.239 m, respectively, with improvement rates of 19.7% and 21.8%.

low-cost device  /  non-line-of-sight (NLOS) signal  /  inertial navigation system (INS)  /  fish-eye camera  /  precise point positioning (PPP) optimization
李林阳, 游嘉伟, 崔阳, 周庆勇, 徐振邦. 低成本设备NLOS信号识别和PPP优化方法. 大地测量与地球动力学, 2026 , 46 (1) : 9 -16 . DOI: 10.14075/j.jgg.2025.02.037
Linyang LI, Jiawei YOU, Yang CUI, Qingyong ZHOU, Zhenbang XU. NLOS Signal Identification and PPP Optimization Method for Low-Cost Device[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (1) : 9 -16 . DOI: 10.14075/j.jgg.2025.02.037
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近年来，随着我国北斗导航卫星系统(BDS)的全面建成、美国全球定位系统(GPS)和俄罗斯格洛纳斯导航卫星系统(GLONASS)的现代化进程持续推进，以及欧盟伽利略系统(Galileo)的不断发展完善，全球导航卫星系统(GNSS)已进入多星座融合互补的新时代。多GNSS星座的融合显著提升了观测冗余度，优化了观测几何构型，能够为用户提供更加连续、稳健和可靠的定位服务[1-2]。
然而，面对日益复杂多样的应用需求，GNSS在城市峡谷、高架林立、林荫遮蔽等复杂城市环境下的性能却严重退化甚至不可用，极大地影响了GNSS定位性能，无法保证其服务的可用性、连续性和可靠性[3-4]。多路径效应是城市遮挡场景下影响GNSS定位性能的重要因素之一，可导致数十米甚至上百米的定位误差[5]。
在遮挡场景下，GNSS信号容易受到周围建筑物等的衍射、反射，形成视距(LOS)信号、非视距(NLOS)信号和多路径信号，如图 1所示。NLOS信号和多路径信号均属于多路径效应的范畴，两者的区别在于：多路径信号是由反射或衍射等造成的间接信号与直接信号的叠加，而NLOS信号则仅包含衍射或反射等间接信号[6]。NLOS信号引起的测距误差通常远大于多路径信号，严重时可能导致数百米甚至无界的定位误差。因此，研究在强干扰、低成本、高时效等条件下的NLOS信号处理，是GNSS高精度、高可靠应用面临的难点问题和关键所在，也是进一步拓展其应用范围的突破口[7]。
传统的天线和接收机改进方法虽然能够部分减弱多路径误差的影响，但会大幅增加硬件成本，主要应用于高精度的大地测量领域。而基于机器学习的NLOS信号识别方法虽然无需额外硬件设备，但其性能受限于训练标签的质量和数量[8-9]。近年来，多源传感器的快速发展和普及为GNSS NLOS信号的高效识别提供了新的机遇。其中，视觉传感器具有自主无源、廉价、功耗低的特点，能够实时感知周围丰富的环境信息，从而实现对NLOS卫星的探测[7, 10]。与离线存储、占用空间大且更新缓慢的三维城市模型[8, 11]相比，视觉传感器的现势性更强，能够实时反映树木、路牌等动态遮挡信息。同时，惯性传感器能够持续为载体提供高精度的位置、速度和姿态信息。因此，如何融合视觉/惯性传感器的观测数据以识别NLOS信号，提升城市遮挡环境下GNSS的定位精度，已成为亟待解决的关键难题。尽管已有研究采用鱼眼相机识别NLOS信号[12-13]，但这些研究仅涉及GPS和BDS卫星，且仅讨论了对伪距定位的影响，尚未探究低成本天线和接收机在多系统(如GPS/BDS/Galileo/GLONASS)NLOS信号下的表现。然而，城市大众用户通常配备低成本板卡、可穿戴设备等，这些设备的观测噪声可达数米甚至数十米[14]，更容易受到NLOS信号的影响。此外，对于低成本设备，NLOS信号的识别和优化方法也变得更加复杂且难以处理。
针对复杂城市环境下低成本GNSS设备受NLOS信号影响的问题，本文提出一种基于惯性导航系统(INS)和鱼眼相机的NLOS信号识别方法，并根据识别结果优化了精密单点定位(PPP)解算策略。实验采用2组实测车载数据，划分了8种实验场景，验证所提算法的有效性。
1 基于INS和鱼眼相机的NLOS信号识别方法
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鱼眼相机视角范围大，可感知丰富的周围环境信息；INS能够持续高精度地为载体提供位置、速度和姿态信息。综合2类传感器的优点，提出一种基于INS和鱼眼相机识别NLOS信号的方法，流程如图 2所示(图中红色、黄色、蓝色、绿色和青色圆点分别代表GLONASS、GPS、QZSS、BDS和Galileo卫星)。流程分为以下4步：
1) 利用朝向天空的广视角鱼眼相机拍摄天空图，并利用GNSS和INS组合解算出车辆的位姿。
2) 对鱼眼相机拍摄的图片，采用天空分割算法分粗分割、误分割去除和分割优化3步，划分天空区域和遮挡区域，获取周围环境遮挡信息。
3) 根据星历提供的GPS、BDS、Galileo、GLONASS等系统的卫星位置，采用INS提供的位姿较正、鱼眼畸变、相机内参和外参，将卫星位置由地心地固系依次转换至当地水平坐标系、相机坐标系、像平面坐标系和像素坐标系，计算卫星在鱼眼图像中的投影。
4) 基于上述天空分割图和卫星坐标投影图，统计投影坐标范围内非天空区域的占比，确定是否为NLOS信号。
1.1 天空分割算法
对所拍摄的鱼眼图像进行天空分割处理，提取天空区域和遮挡区域，具体步骤如下：
1) 基于大津法的天空粗分割。大津法(OTSU)又称最大类间方差法，它借助图像中要提取的目标物体和背景在灰度上的差异，选择合适的阈值，通过判断每一个像素点的特征属性，将图像分割成背景与目标2类。由此可见，确定一个最优阈值是图像分割的关键。具体步骤如下：
假定一幅大小为m像素×n像素的图像，共有L个不同的灰度级。目标与背景的分割阈值为T，目标点数占图像的比例为PO，平均灰度为μO；背景点数占图像的比例为PB，平均灰度为μB。图像的总平均灰度为：
$\mu=P_{\mathrm{O}} \times \mu_{\mathrm{O}}+P_{\mathrm{B}} \times \mu_{\mathrm{B}}$
类间方差表示为：
$\sigma^2=P_{\mathrm{O}} \times\left(\mu_{\mathrm{O}}-\mu\right)^2+P_{\mathrm{B}} \times\left(\mu_{\mathrm{B}}-\mu\right)^2$
从图像的最小灰度级0到最大灰度级L遍历阈值T，当目标函数类间方差σ2取最大值时，T即为所求的OTSU图像分割阈值。
根据OTSU阈值对鱼眼天空图像进行二值化分割。图 3展示了图像进行大津法分割前后的效果对比，分割后的白色目标像素被认为是天空区域，黑色背景像素被认为是遮挡物，鱼眼图像中的天空区域和遮挡区域被粗略提取出来。进一步结合实际的场景情况，发现也存在误分割现象，比如高架桥的防护栏被分割成天空，使天空区域发生了割裂。
2) 基于形态学开运算的误分割去除。利用OTSU对鱼眼图像进行天空粗分割后，图像中会存在孤立细小的孔洞，使得天空区域不再连续。形态学操作中的开运算能够去除图像中的孤立点、毛刺和连接2块区域的小点，因此可用来消除大津法粗分割后图像中的误分割区域。
灰度形态学的膨胀是以某像素(x, y)为中心，取局部灰度最大值，表达式为：
$\begin{gathered}\operatorname{dst}(x, y)=\max \nolimits_{\left(x^{\prime}, y^{\prime}\right): \text { element }\left(x^{\prime}, y^{\prime}\right) \neq 0} \operatorname{src}(x+ \\\left.x^{\prime}, y+y^{\prime}\right)\end{gathered}$
式中，src(x, y)为图像原始像素值；dst(x, y)为形态学处理后的像素值；element为m×n的模板。
腐蚀与膨胀的操作相反，为求图像灰度的局部最小值，表达式为：
$\begin{gathered}\operatorname{dst}(x, y)=\min\nolimits_{\left(x^{\prime}, y^{\prime}\right): \text { element }\left(x^{\prime}, y^{\prime}\right) \neq 0} \operatorname{src}(x+ \\\left.x^{\prime}, y+y^{\prime}\right)\end{gathered}$
首先对图像进行灰度腐蚀，去除细小孤立的噪声区域，然后进行灰度膨胀，填充较小的孔洞。图 4展示了形态学开运算处理后的天空图像效果，经误分割去除处理后，高架防护栏的孔洞得以去除，使得天空区域更加连续，但还存在一些狭长的遮挡物如交通指示牌杆、电线杆等需进一步优化处理。
3) 基于形态学闭运算的分割优化。天空粗分割和误分割去除后，图像中的白色目标像素已基本表示天空区域，黑色背景像素则表示建筑物和其他的大型障碍物。本文的主要目标是识别由大型障碍物引起的遮挡，所以小型遮挡物将被视为噪声。形态学操作中的闭运算能够连接邻近物体，平滑天空区域边界，达到去除噪声的目的。
首先对图像进行灰度膨胀，消除小型遮挡物等噪声点；然后进行灰度腐蚀，还原图像的主要形态。图 5展示了形态学闭运算处理后的天空图像效果，经分割优化处理后，狭长的交通指示牌杆子得以去除，而保留了交通指示牌这一大型遮挡物，与实际情况较为吻合。
1.2 卫星坐标投影
卫星坐标投影实质上是将卫星在地心地固系下的三维空间坐标，经一系列坐标旋转、平移、缩放变换，投影至像素坐标系下的二维像素点，最终形成卫星轨迹天空图的过程。投影流程如图 6所示，可概述为：
1) 利用观测数据和多系统星历计算地心地固坐标系下的卫星坐标，结合GNSS和INS组合解算出的接收机位置，将站星向量转换至导航坐标系下。
2) 结合相机外参，将导航坐标系下的待投影点，经GNSS和INS组合解算出的姿态校正后转换至相机坐标系下。
3) 利用鱼眼畸变改正，将相机坐标系下的空间坐标点投影为图像物理坐标系下的平面坐标点。
4) 利用鱼眼相机内参，将像平面坐标系下的投影点转换至像素坐标系下，最终形成天空投影图。
1.3 NLOS信号识别准则
天空图像分割和卫星坐标投影处理后，卫星被投影为天空分割图像中的像素点，需结合一定的识别准则，给出卫星被建筑物或其他障碍物遮挡的判定条件，从而判断该卫星信号是否为NLOS信号。
如图 7所示，以卫星在天空分割图像上的投影点(x, y)为圆心，r为半径的圆盘为模板原子，通过统计模板原子内非天空区域的比重，判定卫星是否为NLOS类型。计算公式如下：
$c\left(x^{\prime}, y^{\prime}\right)=\left\{\begin{array}{l}1, g\left(x^{\prime}, y^{\prime}\right)=0 \\0, g\left(x^{\prime}, y^{\prime}\right)=255\end{array}\right.$
统计模板原子N内每一像素的灰度值，并将统计结果进行相加，得到结果的总和：
$\left\{\begin{array}{l}n(x, y)=\sum\limits_{\left(x^{\prime}, y^{\prime} \in N\right)} c\left(x^{\prime}, y^{\prime}\right) \\N=\left\{\left(x^{\prime}, y^{\prime}\right) \mid\left(x^{\prime}-x\right)^2+\left(y^{\prime}-y\right)^2 \leqslant r^2\right\}\end{array}\right.$
式中，总数n是非天空区域的像素个数。记nmax为n可能取到的最大值，则卫星属于NLOS类型的置信度分数score为：
$\text { score }=1-\frac{n}{n_{\max }}$
比较置信度分数score与预先设定阈值θ，判定卫星是否为NLOS类型：
$\text { label }=\left\{\begin{array}{l}1, \text { score } \geqslant \theta \\0, \text { score }<\theta\end{array}\right.$
实验中, 设置θ=0.8，r=10像素[15]。
2 基于NLOS识别的PPP定权模型优化
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在遮挡较为严重的城市环境中，若直接将NLOS信号剔除，会使可用卫星数进一步减少，造成卫星空间几何构型产生严重畸变，大大降低定位精度，甚至无法定位。
为削弱NLOS观测量对PPP解算的影响，对识别出的NLOS观测值方差进行膨胀，通过构造等价权函数对NLOS观测值进行降权处理，提升卡尔曼滤波解算时抗NLOS信号的性能。对于第i颗卫星，本文构造的等价权函数为IGGⅢ模型[11, 16]：
$\bar{p}_i=\left\{\begin{array}{l}p_i, \left|\tilde{v}_i\right| \leqslant k_0 \\p_i \frac{k_0}{\left|\tilde{v}_i\right|}\left(\frac{k_1-\left|\tilde{v}_i\right|}{k_1-k_0}\right)^2, k_0<\left|\tilde{v}_i\right| \leqslant k_1 \\0, \left|\tilde{v}_i\right| \geqslant k_1\end{array}\right.$
其中
$p_i=k \cdot\left(a+b \exp \left(-\frac{\theta}{\theta_0}\right)\right)^2 \cdot\left(c \times 10^{-\mathrm{SNR} / 10}\right)$
式中，pi为联合信噪比和高度角确定的观测值权重[17]；a=0.13 m，b=0.56 m，c=1.1×104m2/s，θ0=0.174 5 rad；k为伪距和载波相位之间的比例因子；$\bar{p}_i$为构造等价权函数优化处理后的观测值权重；$\tilde{v}_i=v_i / \sigma_{v_i}$为标准化残差，$v_i$和$\sigma_{v_i}$分别为观测值残差和单位权中误差；k0和k1为常量，取值范围通常分别为1.0~1.5和2.5~8.0。
由式(9)可知，经过降权处理，削弱了NLOS观测值对PPP的影响，且定位残差vi越大，降权效果越明显。
3 实验分析
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3.1 实验平台与数据介绍
实验平台如图 8所示。平台由战术级惯导星际惯性SNC300、和芯星通GNSS板卡UB4B0、低成本华信螺旋天线、低成本u-blox F9P板卡、MEMS惯导ADIS16490和FLIR鱼眼相机BFS-PGE-16S2组成。采用高精度时间同步板卡将不同传感器的时间统一至GPST，使用Kalibr标定工具箱[18]和黑白棋盘格标定了鱼眼相机的畸变参数以及鱼眼相机与IMU的外参。在开阔区域架设了GNSS基准站，使用商用GNSS/INS后处理软件Inertial Explorer(IE)9.0，采用GNSS板卡UB4B0和战术级惯导SNC300，基于实时动态定位(RTK)和惯导的双向平滑结果作为轨迹参考值。
实验采集了复杂城市环境下的多种典型场景(数据集信息如表 1所示)，包括单边遮挡、城市峡谷、单边建筑+单边树木侧方遮挡、双边建筑+单边树木遮挡、连续过桥、桥梁遮挡、双边林荫遮盖、双边林荫遮盖和建筑遮挡、开阔环境等9个场景，其中双边林荫遮盖是指除两侧树木遮挡，顶部还有树木遮挡。
图 9给出车载实验过程中的可用卫星数和位置精度因子(PDOP)值。大部分场景的GNSS观测条件良好，但城市环境下观测卫星数量的变化较为剧烈，在GNSS信号遮挡较为严重的情况下，可用卫星数骤减，甚至因全部遮挡导致GNSS信号短时中断。
3.2 不同场景下NLOS信号识别结果分析
选取单边遮挡、桥梁遮挡、城市峡谷、双边林荫遮盖和建筑遮挡4个场景，天空分割结果见图 10。由图可见：
1) 天空粗分割后，基本可以区别出天空区域和遮挡区域。
2) 误分割去除后，可以消除图像中孤立的孔洞，如桥梁遮挡场景中的高架桥隔音玻璃、双边遮挡场景中的广告牌等。
3) 分割优化处理后，图像中非障碍物遮挡的噪声像素点可以被过滤掉，使得天空区域更加平滑连续。
在城市峡谷、双边林荫遮盖和建筑遮挡2个场景下，图 11(C: BDS, 绿色圆点; G: GPS, 黄色圆点; R: GLONASS, 红色圆点; E: Galileo, 青色圆点; J: QZSS, 蓝色圆点)进一步展示了基于INS和鱼眼相机的NLOS信号识别结果。可以看出，在遮挡较为严重的城市场景中，NLOS卫星数量显著增多，甚至超过了LOS卫星的数量。这是由于环境中有密集的高楼、桥梁等大型建筑物，开阔的天空区域面积相较于遮挡区域的面积更小，NLOS信号发生的概率更大。经统计，城市峡谷场景下NLOS卫星共13颗，LOS卫星共12颗(4C、1E、3G、2R、2J)；双边林荫遮盖和建筑遮挡场景下NLOS卫星共17颗，LOS卫星共9颗(3C、1E、3G、2R)。因此，在城市峡谷、双边林荫遮盖和建筑遮挡等复杂遮挡场景下，NLOS信号带来的多径误差将占主导地位，会极大影响GNSS的定位性能。
根据表 1中的场景01~08，表 2进一步统计了不同场景下的LOS与NLOS信号的识别结果。可以发现：
1) NLOS信号出现的比例受场景的影响较大，不同场景下的LOS与NLOS信号的比例差别较大。
2) 双边林荫遮盖和建筑遮挡中的NLOS比例最高，超过了50%，这是由于该场景下四周及天顶存在密集的树木全向遮挡，而且单边还有建筑物遮挡，GNSS观测环境恶劣。
3) 桥梁遮挡场景的NLOS信号比例最小，仅为5.8%，这是因为除过桥时卫星信号受到短时遮蔽外，其余时段车辆均处在较为开阔的场景。
3.3 PPP精度提升效果分析
利用低成本螺旋天线和u-blox F9P接收机采集的动态观测数据，采用GNSSer软件[19]评估多系统PPP的性能提升效果，具体的解算策略如表 3所示。
图 12展示了4种城市遮挡场景下PPP优化前后的E、N和U分量的定位偏差、可用卫星数和PDOP值序列。可以看出：
1) 图 12(a)单边遮挡场景的周内秒376 460 s和376 600 s，原始PPP误差出现了跳变和发散，对应的PDOP值也同时呈现出“尖峰”，优化后的PPP则保持了更优的连续性和稳定性。
2) 图 12(b)单边建筑和单边树木遮挡+双边建筑和单边树木遮挡的周内秒376 830 s和图 12(c)桥梁遮挡场景的379 235 s，产生NLOS信号的卫星数骤增；对NLOS信号进行降权，优化后PPP的收敛速度明显加快，误差更趋近于零值。
3) 图 12(d)双边林荫遮盖、双边林荫遮盖和建筑遮挡场景下，优化后的PPP的误差序列整体更加平缓，精度和平稳性均更优。
表 4进一步统计了4种遮挡场景下PPP优化前后的均方根(RMS)误差。4种遮挡场景下优化后的PPP精度较优化前均有所提升。桥梁遮挡和双边林荫环境提升效果最为明显，三维位置误差分别从21.259 m和11.814 m降低至17.071 m和9.239 m，精度提升幅度分别为19.7%和21.8%。其中，水平分量的定位精度在优化后最高达到4 m左右，进一步剔除GNSS信号短时中断导致的重收敛，水平分量的定位误差约为3 m。
4 结语
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城市环境下，NLOS信号是影响GNSS定位的主要误差源之一，尤其是对低成本用户。采用鱼眼相机提供的遮挡信息和INS提供的位姿，设计了天空分割、卫星坐标投影和NLOS信号识别准则；为进一步削弱NLOS信号对低成本设备PPP的影响，提出基于观测值残差的NLOS信号等价权矩阵更新策略。采集2组车载低成本设备观测数据进行实验，结果表明, 所提方法可以实现NLOS信号的精准识别，PPP的精度和稳定性均得到了提升，精度提升幅度约20%。
基金
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  • 国家自然科学基金(42474043)
  • 河南省自然科学基金优秀青年基金(252300421205)
  • 重庆市自然科学基金(CSTB2022NSCQ-MSX1129)
文献
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2026年第46卷第1期
35
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引用本文
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文章信息
doi: 10.14075/j.jgg.2025.02.037
  • 接收时间：2024-12-23
  • 首发时间：2026-10-29
  • 出版时间：2026-01-15
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  • 收稿日期：2024-12-23
基金
国家自然科学基金(42474043)
河南省自然科学基金优秀青年基金(252300421205)
重庆市自然科学基金(CSTB2022NSCQ-MSX1129)
作者信息
    1 信息工程大学地理空间信息学院, 郑州, 450001
    2 联勤保障部队工程大学军事设施系, 重庆, 401331
    3 西安测绘研究所, 西安, 710054
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https://castjournals.cast.org.cn/joweb/ddcl/CN/10.14075/j.jgg.2025.02.037
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species		 占总种数比例
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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