GPS 高程转换的总体最小二乘组合方法

在现有GPS高程转换的研究中,通常只采用单一的解算方法,然而,不同的计算方法由于适用的条件不同,在对同一组数据进行解算时,解算的精度也各有优劣。针对以上问题,文中提出GPS高程转换的总体最小二乘组合方法,对多种不同的高程转换方法进行组合,运用总体最小二乘法计算不同方法的权值,结合实际工程数据,验证该方法的可行性。计算结果表明,提出的方法能够有效地提高高程转换精度。     

改进的距离约束最小二乘模糊度搜索算法

针对短基线最小二乘模糊度搜索算法搜索效率低的缺点,该文提出了一种改善模糊度搜索空间以提高模糊度搜索效率的方法。最小二乘搜索算法通过基线长度范围确定模糊度搜索空间,完成模糊度的搜索和确认。距离约束的短基线模糊度搜索空间是一个空心椭球,各模糊度之间具有相关性,影响模糊度搜索效率。该文通过最小二乘去相关调整方法降低模糊度之间的相关性,减少搜索次数。实验证明,该算法的搜索效率提高了25%以上,且随着基线长度的增加,搜索效率有更多的提升。     

基于部分最小二乘岭估计的粗差定值定位

在利用部分最小二乘原理进行粗差定值定位时,模型的法方程矩阵可能存在病态性,使得到的粗差定值定位结果不可靠。文中针对观测数据包含多个粗差且法方程病态问题,利用岭估计处理病态问题,建立部分最小二乘岭估计的粗差定值定位方法,给出粗差搜索步骤,利用迭代算法实现多个粗差的定值和定位。通过模拟算例分析部分最小二乘法、部分最小二乘岭估计在粗差搜索方面的效果,从另一个角度探讨粗差处理方法,推广现有的误差理论,证明文中方法的有效性。     

基于抗差估计的实时单站GPS同震速度获取方法

基于实时单站GPS载波相位历元差分测速模型,引入抗差最小二乘估计,并根据IGGⅢ方案选择合适的等价权因子来削弱小周跳和粗差对结果的影响,并采用一组静态数据和2011年日本"3.11"地震期间MIZU站的高频数据对算法进行测试。结果表明,在含有小周跳和粗差的情况下,抗差最小二乘估计能够明显改善解算速度,可以实时获取测站毫米级的同震速度。     

一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。     

一种基于灰度块匹配的视野监测算法

针对视野移动方向的判断问题,该文介绍了运动补偿的常用方法及应用,提出了基于灰度块匹配的视野移动监测算法。通过对比各种算法计算复杂度和计算时间,发现灰度投影算法准确率低、计算量较小;块匹配搜索算法中的穷尽搜索算法准确率高,但是计算量较大。为了发挥以上两者算法的优势,将两者结合形成了灰度块匹配算法,并通过实验证实了灰度块匹配算法在实时监测系统中的可行性。     

抗差估计的多波束测深数据内插方法

针对传统多波束测深数据网格化内插方法抗差性不足的问题,该文将抗差估计理论应用于网格化内插方法中。基于不确定度指标建立了距离不确定度联合加权内插模型,在该模型的权函数中引入了等价权,同时给出了等价权设计方案及迭代初值确定原则,以增强模型的抗差性;介绍了一种节点邻域参考点合理选取方法,给出了改进内插法实现的基本步骤,最后通过实测数据对反距离加权法、联合加权法及改进方法内插水深质量进行比较。结果表明,改进方法内插结果要优于另外两种方法,可应用于多波束测深数据网格化处理中。     

Estimating attendance from cellular network data

An automatic estimate of the number of attendees to events happening in the city can provide valuable information to geographic information systems and geo-located applications. We present a methodology to estimate the number of events’ attendees from cellular network data. In this work, we used anonymized Call Detail Records (CDRs) comprising data on where and when users access the cellular network. Our approach is based on two key ideas: (1) we identify the network cells associated with the event location. (2) We verify the attendance of each user, as a measure of whether (s)he generates CDRs during the event, but not during other times. We evaluate our approach to estimate the number of attendees to a number of events ranging from football matches in stadiums to concerts and festivals in open squares. Comparing our results with the best groundtruth data available, our estimates provide a median error of less than 15% of the actual number of attendees.     

Bias estimation and correction for triangle-based surface area calculations

The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data.     

Dasymetric Spatiotemporal Interpolation

This research applies the principles of dasymetric mapping to spatiotemporal interpolation by extending the spatial concepts of zone and area to their temporal analogs of interval and duration, respectively. An example application of dasymetric spatiotemporal interpolation using crime event data is presented. Results indicate that dasymetric spatiotemporal interpolation significantly improves the accuracy of estimates over areal or duration weighting. In addition, even when dasymetric interpolation in either the spatial or temporal dimension is relatively weak, combining dasymetric estimation in both space and time dimensions simultaneously has the potential to amplify the accuracy of the overall dasymetric estimation.