海洋环境移动平台观测技术发展趋势分析

文中论述了海洋环境移动平台观测技术发展现状,强调移动平台观测技术的重要性。通过剖析移动平台观测技术谱系,从顶层设计、关键技术创新、平台载体系统集成及应用技术等方面,分析了未来技术发展趋势,提出了加强我国海洋环境移动平台观测技术的发展建议。     

一种新的北斗Klobuchar模型及其精度分析

针对传统Klobuchar模型电离层延迟修正精度不高的问题,提出一种新的Klobuchar模型,以提高北斗导航系统的精度。利用欧洲定轨中心CODE的全球格网数据作为参考,使用松弛搜索法分别对覆盖中国区域的格网点和9个测站的观测数据进行算例分析。比较两种方法得出,新模型的修正精度相对于广播模型有大幅提高;新模型预报7d内的电离层时延值修正效果也比广播模型有明显改善;格网点上的平均修正精度从67.89%提高到78.44%,观测数据的平均修正精度从69.81%提升至82.34%。     

低轨卫星增强北斗三号导航星座性能分析

针对低轨卫星星座有待合理化设计的问题,深入研究了低轨卫星星座增强北斗三号系统定位性能。分析轨道高度、轨道倾角、星座构型对星座覆盖性能的影响,仿真北斗三号、GPS和3种不同类型的低轨星座,研究各低轨星座与北斗三号、GPS的组合系统在所选7个测站以及全球范围内的可见卫星数和PDOP值分布。结果表明低轨卫星对北斗三号的增强效果主要与低轨卫星数目有关,且不同轨道倾角的组合低轨星座有利于均衡系统在全球范围内的可见卫星数与PDOP值分布。低轨卫星有望通过改善卫星观测几何构型提高北斗三号系统的定位性能,且增强效果与低轨星座构型密切相关。     

基于Google Maps API的船舶自动站监控Web设计

简要介绍了Google Maps JavaScript API技术,并着重阐述了其作为主流的WebGIS技术在船舶自动气象站监控页面设计中的应用,包括：通过JavaScript和AJAX技术请求远程数据服务器获取气象数据、状态数据及GIS信息等,并将返回的JOSN格式数据加载到Google Map上,同时通过Google Maps JavaScript定制化船舶图标并对加载的船舶轨迹进行渲染,使用户需要的监控信息更加直观地显示在监控页面上。本文结果已成功应用到广东省气象局设备全网监控系统之中。     

船舶自动气象站分类标准及仪器布设原则

本文在研究分析各类船舶的基础上,对船舶自动气象站(船舶站)的基本构成和采集要素进行了分类,提出了船舶站构成的分类标准,解决了不同船舶选取安装不同要素自动站选择依据不足的问题,详述了船舶站的构成。以典型船舶站为例,详述了温、压、湿、风、降水、能见度、辐射等观测要素的安装位置选择,为船舶站的设计和安装提供参考依据。     

基于脉冲星计时的航天器自主定位误差估计方法

目前很多学者希望从"X射线脉冲星自主导航"的导航算法研究入手,利用数据分析或仿真进行验证.其中关于航天器自主定位的误差估计方法、误差量级和各种影响因素的讨论,并没有在给定详细初始条件的基础上系统性地说明问题.为了分析上述问题,需从天体测量学的角度出发,对自主定位的各种误差源及其影响因素进行探讨和误差量级的估计.首先,由...     

Chemical contamination of a coral reef by the grounding of a cruise ship in Bermuda

Bulk metal analyses of surficial sediments collected around the Norwegian Crown cruise ship grounding site in Bermuda indicated significant but localized contamination of reef sediments by copper and zinc, caused by the stripping of the tri-butyltin (TBT)-free antifouling (AF) paint (Intersmooth 460) from the underside of the hull. Highest copper and zinc values were found in heavily compacted and red-pigmented sediments inside the impact scar and were comparable to levels found close to slip ways of local boat yards where AF paints from hull stripping and cleaning processes are washed into the sea. The re-distribution of AF contaminated sediments by storms and deposition on nearby reefs constitutes a significant ecological risk that could delay recovery processes and reduce the effectiveness of remediation efforts. Whilst the ecotoxicological effects of AF paint particles interspersed with sediment is unknown, and in need of further study, it is argued that the significance of AF paint contamination of grounding sites has been overlooked.     

Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.     

Volume estimation of small scale debris flows based on observations of topographic changes using airborne LiDAR DEMs

This paper describes a geographic information system(GIS)-based method for observing changes in topography caused by the initiation, transport, and deposition of debris flows using highresolution light detection and ranging(LiDAR) digital elevation models(DEMs) obtained before and after the debris flow events. The paper also describes a method for estimating the volume of debris flows using the differences between the LiDAR DEMs. The relative and absolute positioning accuracies of the LiDAR DEMs were evaluated using a real-time precise global navigation satellite system(GNSS) positioning method. In addition, longitudinal and cross-sectional profiles of the study area were constructed to determine the topographic changes caused by the debris flows. The volume of the debris flows was estimated based on the difference between the LiDAR DEMs. The accuracies of the relative and absolute positioning of the two LiDAR DEMs were determined to be ±10 cm and ±11 cm RMSE, respectively, which demonstrates the efficiency of the method for determining topographic changes at an scale equivalent to that of field investigations. Based on the topographic changes, the volume of the debris flows in the study area was estimated to be 3747 m3, which is comparable with the volume estimated based on the data from field investigations.     

Computing least air pollution exposure routes

Personalized routing counts on traveler’s preferences which are usually based on different criteria, such as shortest, fastest, least traffic, or less expensive (e.g., less fuel cost, toll free). However, people are increasingly becoming concerned about the adverse health effects of exposure to air pollution in chosen routes. Exposures to elevated air pollution concentrations particularly endanger children, pregnant women, elderly people, and people with asthma and other respiratory conditions. Choosing routes with least air pollution exposure (APE) is seen as one approach to minimize the level of pollution exposed, which is a major public health issue. Routing algorithms use weights on segments of road networks to find optimum routes. While existing weights are commonly distance and time, among a few others, there is currently no weight based on APE to compute least APE routes. In this paper, we present a weight function that computes weight based on APE. Two different approaches, geostatistical and non-geostatistical, were used to compute APE weight. Each approach was evaluated, and the results indicate that the APE weight is suitable for computing least APE routes.