走航测深数据的自动采集

本文介绍应用IBM微机实现走航实时水深及定位数据的自动采集系统。给出了联机系统方框图及采集、传送数据的程序流程图。并对IBM-PC/XT微机与测深仪之间的接口、数码转换和数据处理作了简要的叙述。     

Regulation of Navigation Channel on Mouth Bar of Guanhe Estuary

-Based on the feasibility study of developing the navigation resources of the Guanhe River and the model test results of the mouth bar regulation, this paper presents some basic principles for the regulation of the channel on the mouth bar, for instance, the direction of navigation channel should be identical with that of the ebb tide current and the main waves, and perpendicular to the bathymetric contours. The principles for regulating mouth bars are also discussed in this paper.     

海图自动综合中二维平面图形知识的获取

为了保持综合过程的真实性,应当把更多的注意力放在数据的特性上,通过对海图中常见的点、线、面的二维平面图形知识进行分析,可以在电子海图的自动综合过程中,把要素自身的图形特征和地理特征在图形简化时同时考虑,并保持综合过程的真实性.     

GNSS-MR技术用于雪深探测的初步研究

全球导航卫星系统干涉反射测量（global navigation satellite system-interferometric reflectometry,GNSS-IR）技术能探测浅层地表的土壤湿度。针对多系统组合的土壤湿度反演问题,基于轨迹聚类方法,从全球定位系统(global positioning system,GPS)、北斗卫星导航系统（BeiDou satellite navigation system,BDS）、格洛纳斯(GLONASS)、伽利略（Galileo）导航系统的信噪比观测数据中提取多径干涉相位,利用经验模型求解轨迹聚类后的土壤湿度估计值,以加权平均方式得到系统组合后的估计结果。结果表明,BDS、Galileo反演精度相当且优于GPS、GLONASS,基于轨迹聚类的多系统组合土壤湿度估计方法的均方根误差为0.041 4 cm³/cm³,相比于单系统的综合反演精度提升约16.3%,相比于单系统的最佳频段反演精度提升约5.2%,所提方法能有效监测土壤湿度的变化。

     

Galileo系统及其在中国的应用

较详细地介绍了Galileo系统的全面体系结构、频率设计、服务内容及其广泛的应用范围，指出了它与GPS系统相比的优点，并对开发和增强Galileo系统在我国多层次、全方位的应用提出了建议。     

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GPS automatic monitoring system for outside deformation of Geheyan Dam on the Qingjiang River

In this paper, the background of developing GPS Automatic Monitoring System for outside deformation of Geheyan Dam is described concisely. The framework, precision and features of the system are stated in detail. Finally, the prospective application of the system is introduced.     

基于相关分析的组合导航RAIM模型研究

探讨基于相关分析理论的组合导航RIAM方法,并给出单个和多个探测粗差的相关分析检验流程。通过模拟12颗GPS和Galileo组合导航卫星定位系统,分别给单颗和两颗正常卫星加入粗差。仿真结果表明：观测值的影响向量与残差的相关系数可以定位含粗差的卫星并将粗差卫星予以剔除。     

Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing (ISO) cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients (K) as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.     

利用风云3D微波成像仪数据估算北极海冰密集度的精度评价

卷积神经网络（convolutional neural network, CNN）已用于星载全球导航卫星系统反射测量（global navigation satellite system-reflectometry,GNSS-R）海冰检测,其具有数据预处理简单、最大限度保留反射面信息等优势,但已有GNSS-R CNN海冰检测方法研究的数据集时间跨度较小,代表性有限,且未考虑训练集内海水、海冰时延多普勒图（delay-Doppler map, DDM）的比例对方法泛化能力的影响。针对该问题,首先提出一种筛除畸形DDM方法,有效筛除错误数据;然后,设计合适的CNN结构及参数,通过小样本对比实验发现CNN模型在训练集内海水、海冰DDM的比例为1∶1时具有高准确率和最佳泛化能力,并优化数据集选取策略;最后使用2018年全年大样本数据集评估改进的方法在大数据量和大时间跨度时的有效性和可靠性。研究表明,改进的方法通过加强数据质量控制、优化数据集选取策略,提升了CNN海冰检测方法的泛化能力及可靠性,使其更适用于实际应用场景,为海冰消融等研究提供参考。