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Article(id=1239165050225742057, tenantId=1146029695717560320, journalId=1238841944844054536, issueId=1239165048212476136, articleNumber=null, orderNo=null, doi=10.12347/j.ycyk.20250730001, pmid=null, cstr=32406.14.ycyk.20250730001, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753804800000, receivedDateStr=2025-07-30, revisedDate=1755964800000, revisedDateStr=2025-08-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1773369570547, onlineDateStr=2026-03-13, pubDate=1757865600000, pubDateStr=2025-09-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773369570547, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773369570547, creator=13701087609, updateTime=1773369570547, updator=13701087609, issue=Issue{id=1239165048212476136, tenantId=1146029695717560320, journalId=1238841944844054536, year='2025', volume='46', issue='5', pageStart='1', pageEnd='158', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773369570067, creator=13701087609, updateTime=1773617788336, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240206151170707811, tenantId=1146029695717560320, journalId=1238841944844054536, issueId=1239165048212476136, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240206151170707812, tenantId=1146029695717560320, journalId=1238841944844054536, issueId=1239165048212476136, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=66, endPage=72, ext={EN=ArticleExt(id=1239165053086257400, articleId=1239165050225742057, tenantId=1146029695717560320, journalId=1238841944844054536, language=EN, title=Research on Multi-System Multi-Frequency GNSS-IR Sea Surface Height Retrieval Using Smartphones, columnId=1239165052184482030, journalTitle=Journal of Telemetry, Tracking and Command, columnName=Navigation Technology Column, runingTitle=null, highlight=null, articleAbstract=

At present, GNSS-IR sea surface height retrieval technology relies primarily on traditional geodetic GNSS receivers. However, due to their high cost and portability limitations, these devices struggle to meet the demands for low-cost and high-precision applications, which restricts the widespread adoption of this technology. To address this, our study utilizes smartphones for GNSS-IR sea surface height inversion experiments. By employing robust estimation techniques, we integrated multi-system and multi-frequency data to enhance the accuracy of the sea surface height inversion. The results indicate that the smartphone's overall retrieval accuracy is approximately 21 cm, with an average correlation coefficient of 0.96, performance comparable to that of traditional GNSS receivers. The multi-system multi-frequency data fusion further improved the sea surface height retrieval accuracy to 6.8 cm, with a correlation coefficient of 0.996. Compared to the initial retrieval results, this approach significantly enhanced both the temporal resolution and the accuracy of sea surface height retrieval. After smoothing, the accuracy of the fused retrieval results reached 6.1 cm. These findings demonstrate that smartphones can achieve centimeterlevel accuracy in sea surface height retrieval, making them a viable alternative to traditional GNSS receivers for GNSS-IR applications.

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目前，GNSS-IR（全球卫星导航系统干涉反射测量）海面高度反演技术主要依赖于传统大地测量型GNSS接收机，由于这些设备价格昂贵且携带不便，难以同时满足低成本和高精度的需求，限制了该技术的广泛应用。因此，本文利用智能手机开展了GNSS-IR海面高度反演相关实验，并基于稳健估计对多系统多频数据进行融合反演海面高度以提升精度。结果表明：智能手机的整体反演结果精度在21 cm左右，平均相关系数为0.96，与传统GNSS接收机的反演性能相当；多系统多频数据融合海面高度反演结果精度为6.8 cm，相关系数为0.996，与初始反演结果相比，显著提升了海面高度反演的时间分辨率和反演精度，经过平滑后的融合反演结果精度达到了6.1 cm。智能手机的海面高度反演精度为厘米级，可替代传统GNSS接收机用于GNSS-IR海面高度反演。

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王硕　1998年生，硕士研究生。

贺凯飞　1982年生，教授，博士生导师。

姚晨光　1995年生，博士研究生。

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王硕　1998年生，硕士研究生。

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王硕　1998年生，硕士研究生。

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贺凯飞　1982年生，教授，博士生导师。

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贺凯飞　1982年生，教授，博士生导师。

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姚晨光　1995年生，博士研究生。

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姚晨光　1995年生，博士研究生。

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tableContent=null), ArticleFig(id=1239265689467417072, tenantId=1146029695717560320, journalId=1238841944844054536, articleId=1239165050225742057, language=EN, label=Table 1, caption=

Smartphone retrieval results

, figureFileSmall=null, figureFileBig=null, tableContent=
卫星系统信号相关系数MAPEMAE/mRMSE/m
GPSS1C0.973.630.1570.191
S5Q0.964.570.1970.236
BDSS2I0.974.650.20.241
GLONASSS1C0.973.360.150.182
GALILEOS1B0.953.980.1730.211
S5Q0.944.620.2020.239
), ArticleFig(id=1239265689572274676, tenantId=1146029695717560320, journalId=1238841944844054536, articleId=1239165050225742057, language=CN, label=表1, caption=

智能手机反演结果

, figureFileSmall=null, figureFileBig=null, tableContent=
卫星系统信号相关系数MAPEMAE/mRMSE/m
GPSS1C0.973.630.1570.191
S5Q0.964.570.1970.236
BDSS2I0.974.650.20.241
GLONASSS1C0.973.360.150.182
GALILEOS1B0.953.980.1730.211
S5Q0.944.620.2020.239
), ArticleFig(id=1239265689656160760, tenantId=1146029695717560320, journalId=1238841944844054536, articleId=1239165050225742057, language=EN, label=Table 2, caption=

The results of sea surface height retrieved by GNSS receiver

, figureFileSmall=null, figureFileBig=null, tableContent=
卫星系统信号相关系数MAPEMAE/mRMSE/m
GPSS1C0.973.450.1510.178
S2P0.964.200.1810.223
S5I0.964.450.1910.231
BDSS1I0.983.740.1720.198
S7I0.984.200.1850.225
S6I0.984.420.1960.236
GLONASSS1C0.973.420.1440.179
S2C0.973.450.1450.176
GALILEOS1X0.953.830.1670.211
S7X0.954.650.2030.228
S5X0.944.680.2010.241
), ArticleFig(id=1239265689752629760, tenantId=1146029695717560320, journalId=1238841944844054536, articleId=1239165050225742057, language=CN, label=表2, caption=

GNSS接收机反演海面高度结果

, figureFileSmall=null, figureFileBig=null, tableContent=
卫星系统信号相关系数MAPEMAE/mRMSE/m
GPSS1C0.973.450.1510.178
S2P0.964.200.1810.223
S5I0.964.450.1910.231
BDSS1I0.983.740.1720.198
S7I0.984.200.1850.225
S6I0.984.420.1960.236
GLONASSS1C0.973.420.1440.179
S2C0.973.450.1450.176
GALILEOS1X0.953.830.1670.211
S7X0.954.650.2030.228
S5X0.944.680.2010.241
), ArticleFig(id=1239265689941373442, tenantId=1146029695717560320, journalId=1238841944844054536, articleId=1239165050225742057, language=EN, label=Table 3, caption=

Accuracy of inversion results before and after smoothing

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数据平滑方法相关系数RMSE/cm
多系统多频融合结果未平滑0.9966.8
Lowess0.9976.1
), ArticleFig(id=1239265690016870919, tenantId=1146029695717560320, journalId=1238841944844054536, articleId=1239165050225742057, language=CN, label=表3, caption=

反演结果平滑前后的精度

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数据平滑方法相关系数RMSE/cm
多系统多频融合结果未平滑0.9966.8
Lowess0.9976.1
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智能手机多系统多频GNSS-IR海面高度反演研究
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王硕 1 , 贺凯飞 1, 2 , 姚晨光 1
遥测遥控 | 导航技术专栏 2025,46(5): 66-72
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遥测遥控 | 导航技术专栏 2025, 46(5): 66-72
智能手机多系统多频GNSS-IR海面高度反演研究
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王硕1, 贺凯飞1, 2, 姚晨光1
作者信息
  • 1中国石油大学（华东） 青岛 266580
  • 2自然资源部海上丝路海洋资源环境组网观测技术创新中心 青岛 266580

    • 王硕　1998年生，硕士研究生。

    贺凯飞　1982年生，教授，博士生导师。

    姚晨光　1995年生，博士研究生。

Research on Multi-System Multi-Frequency GNSS-IR Sea Surface Height Retrieval Using Smartphones
Shuo WANG1, Kaifei HE1, 2, Chenguang YAO1
Affiliations
  • 1.China University of Petroleum (East China), Qingdao 266580, China
  • 2.Technology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation, Ministry of Natural Resources, Qingdao 266580, China
出版时间: 2025-09-15 doi: 10.12347/j.ycyk.20250730001
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摘要
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目前，GNSS-IR（全球卫星导航系统干涉反射测量）海面高度反演技术主要依赖于传统大地测量型GNSS接收机，由于这些设备价格昂贵且携带不便，难以同时满足低成本和高精度的需求，限制了该技术的广泛应用。因此，本文利用智能手机开展了GNSS-IR海面高度反演相关实验，并基于稳健估计对多系统多频数据进行融合反演海面高度以提升精度。结果表明：智能手机的整体反演结果精度在21 cm左右，平均相关系数为0.96，与传统GNSS接收机的反演性能相当；多系统多频数据融合海面高度反演结果精度为6.8 cm，相关系数为0.996，与初始反演结果相比，显著提升了海面高度反演的时间分辨率和反演精度，经过平滑后的融合反演结果精度达到了6.1 cm。智能手机的海面高度反演精度为厘米级，可替代传统GNSS接收机用于GNSS-IR海面高度反演。

全球卫星导航系统干涉反射测量  /  信噪比  /  海平面高度  /  智能手机  /  稳健估计

At present, GNSS-IR sea surface height retrieval technology relies primarily on traditional geodetic GNSS receivers. However, due to their high cost and portability limitations, these devices struggle to meet the demands for low-cost and high-precision applications, which restricts the widespread adoption of this technology. To address this, our study utilizes smartphones for GNSS-IR sea surface height inversion experiments. By employing robust estimation techniques, we integrated multi-system and multi-frequency data to enhance the accuracy of the sea surface height inversion. The results indicate that the smartphone's overall retrieval accuracy is approximately 21 cm, with an average correlation coefficient of 0.96, performance comparable to that of traditional GNSS receivers. The multi-system multi-frequency data fusion further improved the sea surface height retrieval accuracy to 6.8 cm, with a correlation coefficient of 0.996. Compared to the initial retrieval results, this approach significantly enhanced both the temporal resolution and the accuracy of sea surface height retrieval. After smoothing, the accuracy of the fused retrieval results reached 6.1 cm. These findings demonstrate that smartphones can achieve centimeterlevel accuracy in sea surface height retrieval, making them a viable alternative to traditional GNSS receivers for GNSS-IR applications.

GNSS-IR  /  Signal-to-noise ratio  /  Sea level height  /  Smartphone  /  Robust estimation
王硕, 贺凯飞, 姚晨光. 智能手机多系统多频GNSS-IR海面高度反演研究. 遥测遥控, 2025 , 46 (5) : 66 -72 . DOI: 10.12347/j.ycyk.20250730001
Shuo WANG, Kaifei HE, Chenguang YAO. Research on Multi-System Multi-Frequency GNSS-IR Sea Surface Height Retrieval Using Smartphones[J]. Journal of Telemetry, Tracking and Command, 2025 , 46 (5) : 66 -72 . DOI: 10.12347/j.ycyk.20250730001
正文
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0　引言
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海平面高度作为研究海洋变化的最重要参数之一，可用于海洋动力研究、海平面变化研究及海洋环境气候监测等领域[1−3]。因此，准确且高效地获取海平面高度变化信息对于深入研究海洋变化机制至关重要。随着全球导航卫星系统(Global Navigation Satellite System, GNSS)产业的不断发展，GNSS干涉反射[4](GNSS Interferometric Reflectometry，GNSS-IR)技术得益于其有全天候、低功耗、数据源丰富、方便快捷等优势，已成为测量海平面高度变化的常用方法。
从GNSS-IR概念被提出的时间至今，国内外学者对GNSS-IR测高技术的相关理论与算法进行了大量的研究[5−8]。但由于GNSS-IR技术使用的设备多为测量型GNSS接收机，价格昂贵且携带不便，采用低成本、易携带的GNSS设备便成了一个新的研究趋势[9]。文献[10]利用低成本GPS信号接收器成功反演了海面高度，均方根误差(Root Mean Square Error, RMSE)为5.7 cm，验证了低成本GNSS设备在该领域的可行性。文献[11]通过对比实验发现，Android(安卓)平板在潮位反演性能上与大地测量型GNSS接收机相当，均能达到厘米级的反演精度，表明搭载GPS定位芯片的安卓智能设备同样适用于海面高度反演。文献[12]基于Arduino开源平台和简单设备，成功研制了一套GNSS-IR水面高度反演系统，并在巴西Guaíba湖开展了一年的实验验证工作，表明该系统的反演结果与实测的水位数据相关系数高达0.989，日平均均方根误差仅为2.9 cm，进一步证实了低成本GNSS-IR技术在实际应用中的可靠性与精度。最近几年也有学者用智能手机进行了高度测量实验，文献[13]对Android手机的测高结果进行了深入研究，发现Android手机由于线极化天线的特性，可利用的GNSS数据范围更大。文献[14]利用手机和低成本GNSS接收设备进行了GNSS-IR测高实验，经多系统融合后的手机测高结果平均值与实测值进行比较，仅相差2.4 cm。此外，文献[15]还利用低成本智能手机在阿根廷沿海开展了长达27个月的GNSS-IR研究，结果表明，低成本GNSS-R设备可独立应用于监测海面的变化。
考虑到目前对智能手机用于反演海面高度的相关研究相对较少，本文也将基于智能手机和GNSS接收机开展海面高度反演研究，对智能手机的反演性能进一步分析，并对其反演结果进行最优估计[16,17]，使低成本智能手机在GNSS-IR海面高度反演领域尽可能地发挥其优势。
1　海面高度反演原理与方法
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1.1　海面高度反演原理
GNSS-IR技术反演海面高度的原理如图1所示。
GNSS卫星和接收机天线之间的距离可达几万公里，远远大于接收机天线和海平面之间的距离，因此可把进入接收机的直射信号和照射海面的直射信号看作平行直线，把海平面看作镜面，根据反射原理，反射信号到达接收机的距离比直射信号多出Δρ。从图1中可以看出，接收机至海平面的高度距离h与几何延迟Δρ存在以下关系，即
式中，e为直射信号与海平面之间的夹角，也就是卫星高度角，而几何路径延迟与直反射信号相位有如下关系：
式中，λ为载波波长，φr为反射信号相位，φd为直射信号相位。转换为相位延迟可得
根据文献[18]，接收机接收到的SNR(信噪比)信号可表示为
式中，Pn为噪声功率，(Pd+Pr)Pn-1为直射和反射信号非相干项，剩余项为干涉信号引起的SNR振荡项。由于反射信号在传播途径中受到反射介质、极化方式等因素的影响，到达接收机时远远小于直射信号功率(Pr<<Pd)，可以忽略不计，所以对上式去除趋势项后可得
可发现h与SNR振荡项的角频率ω有关，即
从上式中可以看出接收机距离海面的高度h与SNR振荡项的频率成正比。因此在得到振荡频率后即可计算出高度。最后用基准高度减去接收机至海面的高度即可得到实际的海面高度：
1.2　多系统多频数据融合反演海面高度方法
稳健估计(Robust Estimation)可以对来自不同系统的数据进行统一处理，通过合理分配权重等方式，有效地融合多系统数据，可提高反演的精度和可靠性。另外，当不同卫星系统的数据之间存在异常差异时，稳健估计能够识别并处理这些异常数据，并降低该数据在融合计算中的权重，确保融合反演结果的准确性。
GNSS各个系统和各个频段的SNR数据都可以按照GNSS-IR海面测高原理进行海面高度反演。对于产生的多系统多频GNSS-IR海面高度反演值，本文采用稳健估计的方法，并顾及海面动态变化产生的误差，对多种类型的数据进行融合反演海面高度。首先设定一个合理的时间窗口以保证有足够多的反演点能够满足方程组求解，在第i个时间窗内建立观测方程：
式中，为第i个窗口第j个观测值的静态海面反演高度，tan(ei,j)为第i个窗口第j个观测值卫星高度角的正切值，为第i个窗口第j个观测值卫星高度角的变化率，ti,j为第i个窗口第j个观测值的历元，ti为第i个窗口的中点历元，为ti时刻的海面高度改正值，hi为ti时刻的实际海面高度。
上式可以简化为矩阵形式：
式中，，。
公式(9)是只有两个未知数的方程组，可通过最小二乘法解算：
式中，Pi代表第i个窗口内观测值的权重矩阵，Vi为第i个窗口内观测值的残差向量，不同信号具有不同的权重，本文采用基于IGG Ⅲ(抗差估计)权函数的稳健估计模型对不同观测值进行赋权[19]，通过迭代求解得到最佳估值。IGG III权函数可表示为：
式中，k代表迭代次数，Pi是窗口内第i个观测值的权，Vi为残差，数值等于观测值改正数与中误差的比值，k0和k1为常数，通常取k0∈[1.0,3.0]，k1∈[2.8,8.5]。
第一次迭代根据初始最小二乘解算的残差向量对观测值赋权，接下来的迭代过程中，精度较低的观测值的权重将被逐渐削弱，并得到新解，直至满足一定条件时停止迭代，本文的迭代停止条件为：
式中，ε是阈值，通常取0.001，至此可以得出稳健估计后的海面高度。
2　实验分析
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2.1　实验介绍
为了验证低成本安卓智能手机反演海面高度的可行性，在山东省青岛市黄岛区开展了实验，实验地点位于唐岛湾湿地公园东侧的海边。实验设备安置在一个向海面延伸约50米的栈桥上，栈桥四周均为开阔海面，潮汐特征明显，海面较为平静。实验从2024年10月10日14时33分开始，期间天气均为晴天，风速不超过5 m/s，持续到2024年10月12日06时25分。如图2所示，智能手机斜向下放置，手机距海面的平均高度约为4.5 m。实验所采用的智能手机型号为华为P30 Pro，内置定位芯片，可接收GPS(全球定位系统)、GLONASS(格洛纳斯卫星导航系统)、BDS(北斗卫星导航系统)、GALILEO(伽利略卫星导航系统)以及QZSS(准天顶卫星系统)信号。本实验通过GEO++ RINEX Logger APP(智能手机GNSS原始观测数据记录软件)对手机观测到的GNSS导航数据进行记录并存储，实验持续时间约40 h，期间不间断采集观测数据。
另外，在手机设备旁架设了大地测量型GNSS接收机作为对比。雷达测高仪安置在GNSS接收机后方约1 m处，测高仪的精度为3 mm，时间采样间隔也为1 s，可高精度地观测海面的实时变化，作为海面高度反演结果的参考值。雷达测高仪至GNSS接收机之间的垂直距离为1.45 m，至手机的垂直距离为1.41 m。
图3是基于谷歌卫星地图所绘制的P30 Pro手机GPS L1波段的第一菲涅尔反射区，从图中可以看出在方位角及高度角较大的区域会有一部分来自陆地的反射信号进入GNSS接收设备，为确保反射信号全部来自海面，设置有效反射区域方位角为50°~281°、高度角为5°~20°。因此，本实验选取符合上述方位角和高度角的观测数据进行处理。
2.2　结果分析
2.2.1　不同系统单频反演结果
智能手机的海面高度反演结果如表1所示。表中分别为不同频率信号下的SNR数据手机测高结果与雷达测高仪实测海面高的相关系数、平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)、平均绝对误差(Mean Absolute Error, MAE)和均方根误差(Root Mean Squard Error, RMSE)。由表1可知，P30pro手机四个GNSS系统的反演结果与实测海面高的相关系数均大于0.9，整体精度也在0.21 m左右；其中GLONASS系统S1C信号反演的精度最优，RMSE为0.182 m；其次是GPS系统S1C信号，RMSE为0.191 m；四系统中精度最低的是BDS系统S2I信号，RMSE达到了0.241 m。
图4详细展示了不同GNSS系统的反演结果与雷达测高仪实测海面高度的拟合曲线图，整体上反演结果与实测高度较为一致。从图中也可以看出，GLONASS系统的反演结果与海面高度曲线的吻合效果最好，GPS S5Q信号与BDS S2I信号与海面高度曲线的一致性较差。结合表1和图4，可知S1C和S1B信号的反演结果均优于S5Q信号。由于S1C信号和S1B信号的载波频率均为1 575.42 MHz，所在的频段为L1频段，而S5Q信号所在的频段为L5频段。L5频段信号是由新一代导航卫星播发，拥有较大的信号带宽和较强的信号发射功率，而由于多径信号的抑制能力与信号带宽成正比，因此L5信号具有更强的抗多径能力[20]，但对于GNSS-IR技术而言，抗多径能力强的L5频段信号反而不如传统的L1频段信号，因此，从理论上讲L1频段信号的反演结果要优于L5信号。另外，由于华为P30 Pro手机型号较旧，只能接收双频卫星数据和部分北斗卫星信号，而BDS-2卫星信号质量较差，所以可能使其反演结果精度较低。
GNSS接收机反演海面的结果精度统计如表2所示。
从表中可以看出，反演结果与智能手机的结果类似，反演精度最高的GNSS系统是GLONASS卫星，S1C信号和S2C信号均小于0.18 m，且两者几乎相同。GPS S1C信号的精度表现与GLONASS卫星不相上下，RMSE也达到了0.178 m。整体上Galileo卫星的表现最差，RMSE均值接近0.23 m，与最优的GLONASS卫星相比，差值高达0.05 m。不同信号之间的反演结果也具有差异性，在所有信号中，GLONASS S2C信号反演结果与BDS S6I信号结果差异最大，达到了0.065 cm。
2.2.2　多系统多频融合反演结果
对于多系统多频反演结果，将所有信号的初始反演结果放入到一个时间窗内，利用最小二乘动态改正法，即可实现多系统多频反演结果融合。基于稳健估计的多系统多频数据GNSS-IR反演海面高度方法，本文选取了长度为2 h、步长为20 min的滑动窗口对P30 Pro手机设备多系统数据进行融合反演。图5(a)中，黑色实线是实测海面高度变化曲线，淡蓝色圆点是未顾及海面动态变化改正的多系统多频数据反演结果，红色圆点是顾及海面动态变化改正后的融合反演结果，可以看出经改正后融合反演的结果与实际海面高度变化一致性高，特别是在海面动态变化剧烈的时间段，如图中曲线波峰、波谷处，改善效果更加明显。具体来说，未进行改正前的多系统多频数据反演结果与实测海面高度之间的RMSE为21.5 cm，相关系数为0.964；改正后的多系统多频数据融合反演结果与实测海面高之间的RMSE为6.8 cm，相关系数为0.996，反演结果精度提升了68.8%。
融合后的反演结果虽然显著提升了反演精度，但仍存在一定的误差，为了进一步提高数据融合反演后的结果精度，在此基础上采用Lowess(局部加权回归)平滑方法处理融合后的反演值。表3和图6是平滑前后的结果，从图中可以看出，平滑后的结果更贴近海面高度曲线，融合反演精度也较未平滑前提升了约11%，选择合理的平滑方法能够并进一步提高反演精度[21]。
3　结论
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本文开展了海面高度反演实验，利用稳健估计实现了多系统多频数据融合反演海面高度，证实了智能手机用于GNSS-IR海面高度反演的应用潜力。结果表明：智能手机反演海面高度的整体结果精度在21 cm左右，与传统大地测量型GNSS接收机反演性能相当；基于稳健估计方法对智能手机的多系统多频反演结果进行融合，可明显提升反演结果精度和时间分辨率，融合反演结果与实测海面高之间的RMSE为6.8 cm，较初始反演结果精度提升了约70%，对融合反演结果进行Lowess平滑处理，可进一步提升反演精度。
基金
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  • 国家自然科学基金(42174021)
  • 中央高校基本科研业务费专项资金资助(24CX02031A)
文献
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2025年第46卷第5期
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doi: 10.12347/j.ycyk.20250730001
  • 接收时间：2025-07-30
  • 首发时间：2026-03-13
  • 出版时间：2025-09-15
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  • 收稿日期：2025-07-30
  • 修回日期：2025-08-24
基金
国家自然科学基金(42174021)
中央高校基本科研业务费专项资金资助(24CX02031A)
作者信息
    1中国石油大学（华东） 青岛 266580
    2自然资源部海上丝路海洋资源环境组网观测技术创新中心 青岛 266580
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