GNSS多天线基线网单历元模糊度同步解算法

提出多天线基线网单历元模糊度同步解算法，其具体实现步骤为：1）根据宽巷组合模糊度易于固定的优点，采用附加已知基线长度约束法同步解算各基线的宽巷模糊度，得到dm~cm级精度的近似基线分量；2）将解算得到的各近似基线分量作为约束，同步解算各基线的基频模糊度，以获取mm级精度的基线矢量。该方法的关键在于检验各历元宽巷模糊度解算的正确性，以获取可靠的近似基线分量，为解算各基线的基频模糊度提供准确的基线先验信息。由于动态情况下各历元观测信息比较少，单纯依赖ratio检验不可靠，提出结合基线误差、单位权中误差、基线网模糊度闭合环及ratio值等对多组宽巷模糊度进行检验，避免ratio值设置不当导致模糊度检验中发生纳伪和弃真问题。实测数据结果证明，该处理方法使得模糊度解算的成功率提高1%~2%，可以获取移动平台更丰富的导航信息，提高其服务能力。     

“一带一路”背景下的中国与巴基斯坦的贸易关系演进及其影响因素

中巴经济走廊是“一带一路”6大经济走廊之一,而贸易畅通是“一带一路”建设的核心环节。研究中巴贸易关系演变及其影响因素、分析其贸易潜力,对推进“一带一路”建设具有重要的示范作用。论文从中巴贸易发展态势、商品结构、空间格局等方面揭示中巴贸易关系,运用随机前沿引力模型分析中巴贸易关系的主要影响因素、并探析其发展潜力,以期为推进中巴经济走廊建设提供科学支撑。研究发现：① 中巴贸易发展迅速,中对巴贸易顺差持续扩大;中国主要出口机械及电气设备等资本密集型产品,主要进口纺织原料及纺织制品等初级产品和劳动密集型产品。② 中国各省份与巴基斯坦的贸易合作存在明显的空间差异,东部沿海省份与巴贸易联系较紧密,西部各省份除新疆外与巴基斯坦贸易额均较小。③ 中国多数省份对巴贸易商品结构发生显著变动,其中,新疆、山东等进出口商品结构多元的省份的变动相对较小。④ 中国各省份的经济发展水平和市场规模对中巴贸易拉动较强;海运距离对中巴贸易规模有显著的负向影响;领土接壤为中巴组织边境贸易提供了良好条件;铁路和水运口岸的建设对中巴贸易具有积极影响。⑤ 中国各省份与巴基斯坦均有较大贸易潜力,内蒙古、云南、广西、陕西等省份的合作潜力更明显。     

A-FPN算法及其在遥感图像船舶检测中的应用

光学遥感图像船舶检测主要面临两个挑战:光学遥感图像背景复杂,船舶检测易受海浪、云雾及陆地建筑等多方面干扰;遥感图像分辨率低,船舶目标小,对于其分类与定位带来很大困难;针对上述问题,在FPN的基础上,提出一种融入显著性特征的卷积神经网络模型A-FPN (Attention-Based Feature Pyramid Networks)。首先,利用卷积提取图像特征金字塔;然后,利用顶层金字塔逐级构建显著特征层,抑制背景信息,通过金字塔顶层的细粒度特征提高浅层特征的表达能力,构建自上而下的多级显著特征映射结构;最后利用Softmax分类器进行多层级船舶检测。A-FPN模型利用显著性机制引导不同感受下的特征进行融合,提高了模型的分辨能力,对遥感图像处理领域具有重要应用价值。实验阶段,利用公开的遥感目标检测数据集NWPU VHR-10中的船舶样本进行测试,准确率为92.8%,表明A-FPN模型适用于遥感图像船舶检测。     

连续台风时期香港区域的水汽变化分析

利用香港卫星定位参考站网GNSS观测数据,提取强热带风暴"塔拉斯"与热带风暴"洛克"影响期间各测站天顶方向对流层延迟,反演香港区域大气可降水量;根据香港区域49个天文台气象站提供的实测降雨量数据,分析大气可降水量与实际降雨量的相关性,以及两次台风对香港区域水汽时空分布的不同影响。结果表明,大气可降水量在台风影响前期均上升,在大量降雨后回落,但在连续台风的间歇期间,仍高于台风来临前的水平;水汽累积是大量降雨的前提条件,当水汽累积量相近时,水汽累积时长与累积降雨量呈正相关;台风期间大气可降水量值超过65 mm的区域面积与台风等级相关,台风路径对局部水汽分布有一定的影响。     

PTP协议在广电时间同步网中的应用研究

随着广电行业的飞速发展,以串行数字接口(SDI)基带信号为基础技术架构的传统电视信号已经很难满足未来技术发展和业务扩展的需求．得益于现代通信技术的迅速发展以及即将到来的万物互联的5G时代,广电行业也将采用IP化技术来构建新的网络系统．处在向IP化发展的过渡期,广电IP化目前还存在一些亟待解决的问题．为了解决广电系统网络中需要更高精度的同步信号来完成时间同步的问题,本文利用精确时间协议（PTP）时间同步技术在专用以太网中完成时间同步组网,搭建了测试环境并对测试数据进行了分析．测试结果表明,相较于传统的网络时间协议（NTP）时间同步技术,输出PTP信号的准确度能够达到亚微秒量级,完全满足广电行业IP化数字电视系统时间同步信号的高精度需求．      

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.     

极地低压对费拉姆海峡大西洋入流水天气尺度变化的影响

The Atlantic inflow in the Fram Strait(78°50′N) has synoptic scale variability based on an array of moorings over the period of 1998–2010. The synoptic scale variability of Atlantic inflow, whose significant cycle is 3–16 d, occurs mainly in winter and spring(from January to April) and is related with polar lows in the Barents Sea. On the synoptic scale, the enhancement(weakening) of Atlantic inflow in the Fram Strait is accompanied by less(more)polar lows in the Barents Sea. Wind stress curl induced by polar lows in the Barents Sea causes Ekman-transport,leads to decrease of sea surface height in the Barents Sea, due to geostrophic adjustment, further induces a cyclonic circulation anomaly around the Barents Sea, and causes the weakening of the Atlantic inflow in the Fram Strait. Our results highlight the importance of polar lows in forcing the Atlantic inflow in the Fram Strait and can help us to further understand the effect of Atlantic warm water on the change of the Arctic Ocean.     

Using advanced soft computing techniques for regional shoreline geoid model estimation and evaluation

This study aims at evaluating the global geoid model for a regional shoreline fitting using advanced soft computing techniques and global navigation satellite system/leveling measurements. Artificial neural networks, fuzzy logic, and least square support vector machine models are developed and used to fit the global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is designed and evaluated based on the latest global geoid models. The results of the three estimation models show that they can be used to correct the shoreline geoid model, in terms of root mean square error that ranges from 1.7 to 8.5?cm. Moreover, it is found that the least square vector machine model is a competitive approach with certain advantage in solving complex problems represented by missing data.     

Helmert方差分量估计在GPS/GLONASS/BDS组合定位权比确定中的应用

因GNSS系统间观测噪声、轨道精度的差异，采用经验权比进行组合定位难以得到最优结果。基于此，在GPS/GLONASS/BDS组合定位中引入Helmert方差分量估计，对GPS/GLONASS/BDS组合单点定位和基线解算中各系统观测值进行合理定权。实验表明，采用该方法确定的伪距观测值最佳权比为5∶1∶1，相位观测值最佳权比为1∶1∶1，有效提高了GPS/GLONASS/BDS组合定位的精度和可靠性。     

国产高分辨率遥感影像农村公路核查方法及其应用

传统的农村公路核查需要人工实地抽查或通过GNSS设备进行信息采集验核，存在成本高、效率低等问题。遥感影像具有成像范围广、时效性高、成本低、能客观反映现实情况等优点。相比于传统方法，将遥感影像引入农村公路核查，能客观、准确、高效地对农村公路相关信息进行核查。本文基于国产高分辨率遥感影像，结合农村公路遥感核查业务，采用遥感影像道路提取算法，设计并实现了一种农村公路核查方法。将本方法应用于某中部省份农村公路遥感核查业务，实际应用表明该方法能有效提高现有农村公路遥感核查的工作效率。