共查询到18条相似文献，搜索用时 156 毫秒
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考虑实用性和合理性,将线元看成离散点的集合,将线的不确定性看成点的不确定性的聚合体,将线元的位置不确定性模型看成以各点误差椭圆的长半轴E为半径的误差圆的聚合体,建立了以线元上任意点处的误差椭圆的长半轴E为带宽的线元不确定性εE模型。给出了基于该模型衡量线元位置不确定性的三种度量指标:可视化图形、平均误差带宽和误差带的面积。最后,将该模型与εσ模型和εm模型进行了比较。 相似文献
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分别利用直线、圆曲线与多项式曲线的拟合空间曲线实体,估计出拟合曲线与真实曲线之间的模型误差,建立包含模型误差与法线方向位置误差的曲线综合误差带模型。并通过算例证明了含有模型误差的综合误差带模型能更好地反应圆曲线的位置不确定性。 相似文献
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首先研究了线元不确定性的εm模型,将该模型误差带边界线分为左边界线、右边界线、左误差半圆和右误差半圆四部分,利用代数的方法推导了这四部分误差带边界线的解析表达式;利用误差带边界线的解析表达式,绘出不确定性区域的图形.给出了平均误差带宽和误差带的面积作为线元不确定性的精度评估指标. 相似文献
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作者以Mohr圆表示了平面坐标系中点位协因数的各分量。本法不仅能给出任意方向上的自协因数,也可容易表示出两正交方向间的互协因数。Mohr圆不仅全面地反映了一点的误差状态,并可解决位移检验等实际问题。文中还指出,三维空间点坐标的协因数阵存在3个与坐标系无关的不变量。 相似文献
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矢量GIS平面一般曲线等概率密度误差模型的几何特征 总被引：2，自引：0，他引：2
基于等概率密度误差模型建模原理和数值算法,运用函数极值理论和迭代方法来求解平面一般曲线上两相邻特征点间位置精度最高的点,以精确确定误差模型的最小带宽,从理论上给出等概率密度误差模型的几何特征,从而进一步完善矢量GIS的位置不确定性理论。通过实例计算与可视化分析,验证了理论推导的正确性。 相似文献
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文中以直缓点为坐标原点,以线路交点方向为x轴,以半径方向为y轴,建立了一个直角坐标系,推导出了求曲线上任一点切线方向的数学模型.由切线方向即可较容易地导出该点处横断面方向.同时也介绍了在缓和曲线和圆曲线部分,测设横断面方向的方法. 相似文献
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基于折线逼近的曲线位置与模型误差综合建模 总被引：1，自引：0，他引：1
针对目前GIS中曲线常通过一系列折线来逼近的情况,研究考虑由于折线逼近导致的模型误差和由于测量导致的点位随机误差综合影响的曲线不确定性模型。分析曲线拟合的分段准则,提出折线逼近产生的模型误差可由折线模型到真实曲线的垂直距离描述,建立集成模型不确定性与基于误差传播定律的位置不确定性的曲线误差综合量化模型。 相似文献
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岩层与地表移动具有一定的规律,由于Knothe时间函数虽然可以预计地表下沉,但在预计中有描述地表下沉速度的不足,并且其函数复杂。研究矿区单点动态下沉过程对于地表建筑物的保护具有重要意义,本文采用Logistic模型拟合下沉曲线,得到了预计方程,结果表明预计精度较高。Logistic模型参数少,函数相对简单,计算结果表明Logistic模型对矿区开采沉陷具有较好的预测精度。该模型对单点的下沉拟合程度高,在采用81期数据的预计模型中,误差平方和SSE为0.0066218,误差均方MSE为0.0000871,接近于0,表明拟合与预计精度较高,最大预计误差为0.0120673 m,对于研究矿区地表下沉规律具有重要意义,有助于了解地表单点下沉全过程,在地表活动剧烈期可对地表建筑加强支护。 相似文献
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为了提升圆扫描式机载激光测深系统的定位精度,提出了一种检校思路:在平面区域获取激光点云时,系统误差和随机误差使得本应共面的激光点云不再共面,通过将激光点云拟合到单个平面上达到纠正点云位置的目的。首先推导了简单模式下圆扫描式机载激光测深系统定位模型,并从直线与平面交会的数学原理出发模拟激光光线与海面的交会过程,根据折射原理解算激光光线在水中的方向矢量,联合激光光线在水中的直线方程和海底面数学方程解算激光脚点的位置。然后,引入未知数先验方差,推导了参数加权最小二乘平差模型,为后面检校模型的解算奠定基础。最后,推导了用于检校的平差数学模型和详细的计算过程,对检校过程进行了模拟计算和分析讨论,得出了一些有益于检校过程的结论。 相似文献
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Least-squares collocation with covariance-matching constraints 总被引：1，自引：0，他引：1
Christopher Kotsakis 《Journal of Geodesy》2007,81(10):661-677
Most geostatistical methods for spatial random field (SRF) prediction using discrete data, including least-squares collocation
(LSC) and the various forms of kriging, rely on the use of prior models describing the spatial correlation of the unknown
field at hand over its domain. Based upon an optimal criterion of maximum local accuracy, LSC provides an unbiased field estimate
that has the smallest mean squared prediction error, at every computation point, among any other linear prediction method
that uses the same data. However, LSC field estimates do not reproduce the spatial variability which is implied by the adopted
covariance (CV) functions of the corresponding unknown signals. This smoothing effect can be considered as a critical drawback
in the sense that the spatio-statistical structure of the unknown SRF (e.g., the disturbing potential in the case of gravity
field modeling) is not preserved during its optimal estimation process. If the objective for estimating a SRF from its observed
functionals requires spatial variability to be represented in a pragmatic way then the results obtained through LSC may pose
limitations for further inference and modeling in Earth-related physical processes, despite their local optimality in terms
of minimum mean squared prediction error. The aim of this paper is to present an approach that enhances LSC-based field estimates
by eliminating their inherent smoothing effect, while preserving most of their local prediction accuracy. Our methodology
consists of correcting a posteriori the optimal result obtained from LSC in such a way that the new field estimate matches
the spatial correlation structure implied by the signal CV function. Furthermore, an optimal criterion is imposed on the CV-matching
field estimator that minimizes the loss in local prediction accuracy (in the mean squared sense) which occurs when we transform
the LSC solution to fit the spatial correlation of the underlying SRF. 相似文献
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Eryong Liu 《制图学和地理信息科学》2016,43(4):321-327
This article presents a new development in measuring the positional error of line features in Geographic Information Systems (GIS), in the form of a new measure for estimating the average error variance of line features, including line segment, polyline, polygon, and curved lines. This average error measure is represented in the form of a covariance matrix derived by an analytical approach. Corresponding error indicators are derived from this matrix. The error of line features mainly results from two factors: (1) an error propagated from the original component points of line features and (2) a model error of interpolation between these points. In this study, a method of average error estimation has been derived regarding the first type error of line features that are interpolated by either linear or cubic interpolation methods. The main contribution of the research is the provision of an error measure to assess the quality of spatial data in application settings. The proposed error models for estimating average error variance of line features in a GIS are illustrated by both simulated and practical experiments. The results show that the line accuracy from a linear interpolation is better than a line interpolated using a cubic model. 相似文献
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Compactly supported radial covariance functions 总被引：1，自引：0，他引：1
G. Moreaux 《Journal of Geodesy》2008,82(7):431-443
The Least-squares collocation (LSC) method is commonly used in geodesy, but generally associated with globally supported covariance
functions, i.e. with dense covariance matrices. We consider locally supported radial covariance functions, which yield sparse
covariance matrices. Having many zero entries in the covariance matrice can both greatly reduce computer storage requirements
and the number of floating point operations needed in computation. This paper reviews some of the most well-known compactly
supported radial covariance functions (CSRCFs) that can be easily substituted to the usually used covariance functions. Numerical
experiments reveals that these finite covariance functions can give good approximations of the Gaussian, second- and third-order
Markov models. Then, interpolation of KMS02 free-air gravity anomalies in Azores Islands shows that dense covariance matrices
associated with Gaussian model can be replaced by sparse matrices from CSRCFs resulting in memory savings of one-fortieth
and with 90% of the solution error less than 0.5 mGal.
This article is dedicated to Cerbère. 相似文献
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星基增强系统(satellite based augmentation system,SBAS)通过地球同步轨道卫星实时播发导航卫星星历改正数和完好性参数,以提升用户定位精度和完好性。采用最小方差法解算GPS星历改正数,利用卡方统计进行改正数完好性检核,并依据星历改正数方差-协方差信息计算SBAS用户差分距离误差(user differential range error,UDRE)和信息类型28(message type 28, MT28)等完好性参数。利用中国区域27个监测站的实测数据,首先以国际GNSS服务组织的精密轨道和钟差产品为参考解算星历改正数,结果表明,钟差改正精度优于0.1 m,轨道改正精度优于0.4 m;然后解算广播星历改正数,并生成UDRE和MT28参数,广播星历残余误差卡方检验值均小于告警门限,保证了改正数的完好性;最后利用生成的改正数进行SBAS定位解算,得到定位结果的水平精度优于0.7 m,垂直精度优于1.0 m,对比GPS单点定位,所提算法的水平和垂直方向精度分别提升了30%和40%。 相似文献