Calibration of geoid error models via a combined adjustment of ellipsoidal, orthometric and gravimetric geoid height data

The well-known statistical tool of variance component estimation (VCE) is implemented in the combined least-squares (LS) adjustment of heterogeneous height data (ellipsoidal, orthometric and geoid), for the purpose of calibrating geoid error models. This general treatment of the stochastic model offers the flexibility of estimating more than one variance and/or covariance component to improve the covariance information. Specifically, the iterative minimum norm quadratic unbiased estimation (I-MINQUE) and the iterative almost unbiased estimation (I-AUE) schemes are implemented in case studies with observed height data from Switzerland and parts of Canada. The effect of correlation among measurements of the same height type and the role of the systematic effects and datum inconsistencies in the combined adjustment of ellipsoidal, geoid and orthometric heights on the estimated variance components are investigated in detail. Results give valuable insight into the usefulness of the VCE approach for calibrating geoid error models and the challenges encountered when implementing such a scheme in practice. In all cases, the estimated variance component corresponding to the geoid height data was less than or equal to 1, indicating an overall downscaling of the initial covariance (CV) matrix was necessary. It was also shown that overly optimistic CV matrices are obtained when diagonal-only cofactor matrices are implemented in the stochastic model for the observations. Finally, the divergence of the VCE solution and/or the computation of negative variance components provide insight into the selected parametric model effectiveness.     

基于等效残差积探测粗差的方差-协方差分量估计

已有方差-协方差分量估计(VCE)的粗差探测是对残差检验,而VCE的本质是利用残差的二阶量来估计它的二阶中心矩,因此更合理的方法应该对残差二阶量检验.由最小二乘残差估值导出了一组服从标准正态分布的等效残差;然后从等效残差的VCE基本方程出发,分别采用χ2统计量和正态积Np统计量检验等效残差的平方及其乘积.结果表明,采用...     

等价条件平差模型的方差-协方差分量最小二乘估计方法

提出等价条件闭合差的方差-协方差分量最小二乘估计方法，简称LSV-ECM法。首先，利用等价条件平差模型建立了基于等价条件闭合差二次型的方差-协方差分量估计方程，由矩阵半拉直算子将其变换为线性Gauss-Markov形式，进而通过最小二乘准则导出了具有模型通用性、形式简洁性且满足无偏性和最优性的方差-协方差分量估计公式。其次，证明了LSV-ECM方法与残差型VCE方法的等价性，并在此基础上通过计算复杂度定量分析了所提方法的计算高效性。最后，通过边角网平差和中国区域GNSS站坐标时序建模及其结果分析，验证了所提新方法的正确性和计算高效性。     

无缝仿射基准转换模型的方差分量估计

以无缝仿射基准变换为例,研究了无缝基准转换模型的方差分量估计理论,导出无缝基准变换过程中两套坐标的方差分量估计公式。通过模拟试验分析表明,采用方差分量估计方法能正确地恢复出客观体现两套坐标误差的方差值,从而有效提高无缝基准转换精度。     

Variance component estimation uncertainty for unbalanced data: application to a continent-wide vertical datum

Variance component estimation (VCE) is used to update the stochastic model in least-squares adjustments, but the uncertainty associated with the VCE-derived weights is rarely considered. Unbalanced data is where there is an unequal number of observations in each heterogeneous data set comprising the variance component groups. As a case study using highly unbalanced data, we redefine a continent-wide vertical datum from a combined least-squares adjustment using iterative VCE and its uncertainties to update weights for each data set. These are: (1) a continent-wide levelling network, (2) a model of the ocean’s mean dynamic topography and mean sea level observations, and (3) GPS-derived ellipsoidal heights minus a gravimetric quasigeoid model. VCE uncertainty differs for each observation group in the highly unbalanced data, being dependent on the number of observations in each group. It also changes within each group after each VCE iteration, depending on the magnitude of change for each observation group’s variances. It is recommended that VCE uncertainty is computed for VCE updates to the weight matrix for unbalanced data so that the quality of the updates for each group can be properly assessed. This is particularly important if some groups contain relatively small numbers of observations. VCE uncertainty can also be used as a threshold for ceasing iterations, as it is shown—for this data set at least—that it is not necessary to continue time-consuming iterations to fully converge to unity.     

基于等价条件闭合差的VCE通用解析法

分析指出了现有方差-协方差分量估计(VCE)方法在计算效率与?2统计量统计性质两方面的固有缺陷。利用零空间算子消去概括平差模型中的参数向量,建立了等价条件平差模型。由此定义了等价条件闭合差(ECC),并导出了以ECC表示的?2统计量计算式。进而,基于等价条件闭合差与新构造的可逆方差分量模型提出了方差-协方差分量估计的通用解析法,简称为VCE-ECC法。同时,给出了对应四种基本平差模型的VCE-ECC法简化计算式。实例与仿真结果表明：VCE-ECC法与现有VCE方法的方差-协方差分量估计值在统计意义上无明显差异,并有效地克服了现有VCE方法的固有缺陷。     

基于等效残差的方差-协方差分量估计

首先概括性地阐述方差-协方差分量估计(VCE)理论的发展历史与研究现状;利用正交分解提取出等效残差,建立VCE的基本方程,在此基础上阐述VCE算法的局部最优特性和可能出现负定协方差阵估计结果等两大问题,并分析可能的解决方案及其复杂性;导出初值给定的Helmert,最小二乘和MINQUE VCE的线性逼近估计公式,证明基于等效残差的估计公式与已有VCE公式等价,各种估计方法在本质上是一致的;最后,用两个算例验证本文的观点.     

偏差改正的Partial EIV模型方差分量估计

针对Partial EIV模型的方差分量估计中未考虑参数估值偏差所带来的影响,将Partial EIV模型视为非线性函数得到参数估值的偏差及二阶近似协方差表达式,计算得到偏差改正后的参数估值,结合方差分量估计方法,更新由参数估值影响的矩阵变量,给出了基于偏差改正的方差分量估计迭代方法。试验结果表明,参数估值及其协方差主要受参数估值偏差大小的影响,加入偏差改正能够得到更加合理的参数估值及方差分量估值,偏差改正后的方差分量估值可更加合理地评估参数估值的精度信息。     

GPS position time-series analysis based on asymptotic normality of M-estimation

The efficacy of robust M-estimators is a well-known issue when dealing with observational blunders. When the number of observations is considerably large—long time series for instance—one can take advantage of the asymptotic normality of the M-estimation and compute reasonable estimates for the unknown parameters of interest. A few leading M-estimators have been employed to identify the most likely functional model for GPS coordinate time series. This includes the simultaneous detection of periodic patterns and offsets in the GPS time series. Estimates of white noise, flicker noise, and random walk noise components are also achieved using the robust M-estimators of (co)variance components, developed in the framework of the least-squares variance component estimation (LS-VCE) theory. The method allows one to compute confidence interval for the (co)variance components in asymptotic sense. Simulated time series using white noise plus flicker noise show that the estimates of random walk noise fluctuate more than those of flicker noise for different M-estimators. This is because random walk noise is not an appropriate noise structure for the series. The same phenomenon is observed using the results of real GPS time series, which implies that the combination of white plus flicker noise is well described for GPS time series. Some of the estimated noise components of LS-VCE differ significantly from those of other M- estimators. This reveals that there are a large number of outliers in the series. This conclusion is also affirmed by performing the statistical tests, which detect (large) parts of the outliers but can also leave parts to be undetected.     

加权混合估计中权值的确定方法

综合了大地测量中各种异方差多源观测模型和联合平差方法,说明了混合估计方法可以用于测量数据融合,平衡附加信息和样本信息对参数估计的影响。通过求取权值使参数估计的协方差阵的迹最小的方法,给出了一个权的最优选择方法。本文扩展了已有的加权混合估计方法,使得新方法中的权不受验前单位权方差的限制,能有效应用于大地测量数据处理。     

基于等价方差-协方差的粗差探测敏感度分析及实现

选权迭代法在面对独立观测量中的粗差时能够表现出良好的探测效果,但由于其只是一种基于独立观测值的稳健估计法,没有考虑到观测值之间的相关性[1]。而现有的等价权函数虽然都满足稳健估计的要求,但由于所构造的等价权阵不对称,使得最后平差结果严重偏离实际情况。本文介绍在传统稳健估计的基础上,在定权时充分考虑相关观测值之间相关性的不变性,构造对称的方差—协方差阵不断扩大,并通过VB进行编程及实例分析,发现该方法对粗差的敏感度非常强,探测精度很高。     

GPS坐标时间序列噪声估计及相关性分析

比较分析了极大似然估计法、最小二乘方差分量估计法以及最小范数二次无偏估计法对GPS站坐标时间序列噪声的估计效果,确定最小范数二次无偏估计法为最优的噪声方差估计方法。在此基础上对中国及其周边区域20个IGS站坐标时间序列中各方向噪声方差进行一元线性回归分析。结果表明,中国及其周边区域IGS站不同方向的噪声间具有中等强度以上的相关性,其中N方向闪烁噪声与其他方向闪烁噪声的相关性要强于白噪声。水平方向的噪声振幅能够解释垂直方向噪声振幅变化的40%~60%,而N方向噪声振幅能够解释E方向噪声振幅变化的60%~80%,获得的线性回归方程具有使用价值。     

Effects of errors-in-variables on weighted least squares estimation

Although total least squares (TLS) is more rigorous than the weighted least squares (LS) method to estimate the parameters in an errors-in-variables (EIV) model, it is computationally much more complicated than the weighted LS method. For some EIV problems, the TLS and weighted LS methods have been shown to produce practically negligible differences in the estimated parameters. To understand under what conditions we can safely use the usual weighted LS method, we systematically investigate the effects of the random errors of the design matrix on weighted LS adjustment. We derive the effects of EIV on the estimated quantities of geodetic interest, in particular, the model parameters, the variance–covariance matrix of the estimated parameters and the variance of unit weight. By simplifying our bias formulae, we can readily show that the corresponding statistical results obtained by Hodges and Moore (Appl Stat 21:185–195, 1972) and Davies and Hutton (Biometrika 62:383–391, 1975) are actually the special cases of our study. The theoretical analysis of bias has shown that the effect of random matrix on adjustment depends on the design matrix itself, the variance–covariance matrix of its elements and the model parameters. Using the derived formulae of bias, we can remove the effect of the random matrix from the weighted LS estimate and accordingly obtain the bias-corrected weighted LS estimate for the EIV model. We derive the bias of the weighted LS estimate of the variance of unit weight. The random errors of the design matrix can significantly affect the weighted LS estimate of the variance of unit weight. The theoretical analysis successfully explains all the anomalously large estimates of the variance of unit weight reported in the geodetic literature. We propose bias-corrected estimates for the variance of unit weight. Finally, we analyze two examples of coordinate transformation and climate change, which have shown that the bias-corrected weighted LS method can perform numerically as well as the weighted TLS method.     

Gauss-Markov模型参数的岭型广义逆估计及其优良性

本文继续文献 [10 ]的工作 ,进一步讨论了测量平差 Gauss- Markov模型参数岭型广义逆估计的若干性质 ,如允许性、优效性、相对效率、抗干扰性等等 ,得到了许多重要结论。计算结果表明 ,在设计阵呈病态时 ,岭型广义逆估计确能明显改善 L S估计     

Gauss-Markov模型参数的岭型广义逆估计及其优良性

本文继续文献 [10 ]的工作 ,进一步讨论了测量平差 Gauss- Markov模型参数岭型广义逆估计的若干性质 ,如允许性、优效性、相对效率、抗干扰性等等 ,得到了许多重要结论。计算结果表明 ,在设计阵呈病态时 ,岭型广义逆估计确能明显改善 L S估计     

利用偏差改正的方差分量估计方法确定联合反演相对权比

在大地测量联合反演中,方差分量估计法用于确定相对权比时并没有考虑大地测量反演的病态性,利用正则化解代替最小二乘解会引入偏差,会造成方差分量估计不准确问题。针对此提出采用偏差改正方差分量估计方法,以消除正则化解引入偏差的影响,并基于残差的偏差改正方差分量估计与方差分量估计法进行模拟实验。结果表明,进行偏差改正后的方差分量估计法能够较好地反演出滑动分布情况,所提方法针对参数进行偏差改正的方差分量估计考虑了迭代初值引入的偏差,理论更为严密。并将所提方法用于Visso地震和Norcia地震反演中,验证了该方法的可靠性、合理性。     

改进的Helmert方差分量估计方法在精密定轨中的应用

分析了Ｈｅｌｍｅｒｔ方差分量估计方法用于人造卫星精密定轨时出现负方差的原因,认为主要是由于观测资料信息不足而使法矩秩亏引起的,在此基础上,提出了利用等估参数先验信息来避免负方差的“改进的Ｈｅｌｍｅｒｔ方差分量估计方法”。试算表明,利用本文中的方法可以有效地消除负方差的产生。为了与现用的精密定轨软件相兼容,便于程序实现,将ＧｉｖｅｎｓＧｅｎｔｌｉｍａｎ正交变换方法用于方差分量估计,给出了详细的计算分     

利用基于抗差垂直向方差分量估计的GPS-InSAR数据融合方法反演三维形变场

GPS-InSAR数据融合解算三维形变场模型易受观测值粗差影响,且基于方差分量估计的定权方法不具备抵御粗差能力,计算效率低下。鉴于此,本文提出了一种基于抗差垂直向方差分量估计的GPS-InSAR融合解算模型,利用方差分量估计方法及抗差估计理论,通过对观测值最优化分类并进行选权迭代,精确分配权重,进而有效计算三维形变场。试验结果表明,该方法能有效抵御观测值粗差不利影响,提高三维形变场反演精度,提升逐点式计算的三维形变场效率。     

平差参数的抗差特征根估计

在有偏估计的特征根方法基础上 ,利用等价权方法 ,提出一种新的估计—抗差特征根估计。它既能有效地克服设计阵复共线性的影响 ,又可显著地抵抗粗差的干扰。从而提高了参数估值的准确性、稳定性和可靠性。