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。     

抗差岭估计的误差影响测度

当观测值受异常污染影响而不服从正态分布，且平差法方程出现病态时，采用抗差岭估计可得到参数的理想解。本文基于抗差岭估计理论，导出了抗差岭估计的误差影响函数，以及实用的抗差岭估计参数解差和参数解差函数，并结合实例作了多种的试算和比较，结果表明，抗差岭估计的误差影响函数对模型及参数解的理论分析具有重要意义，参数解差函数计算方便，几何意义明确。     

抗差贝叶斯估计及应用

当未知参数具有先验期望和方差，且观测值与未知参数先验值均服从正态分布时，最小二乘贝叶斯估计将给出参数的最优解。然而当观测值和参数先验值的实际分布有悖于正态假设时，经典贝叶斯估计使估值偏高。本文基于常用的M估计原理，对三种类型的误差模式，导出了M－LS、LS－M和M－M三种抗差贝叶斯估计解式和影响函数；讨论了相应的计算方法；给出了参数验后方差表达式。     

多类大地测量数据的融合方法及其比较

本文针对多类大地测量数据处理问题,讨论了多类大地测量数据的抗差解融合、方差分量估计解融合、基于抗差解的方差分量估计融合以及顾及参数先验信息的自适应融合等数据处理方法,采用这些方法能合理地调整不同种类观测量间的权比以及观测量与参数先验信息问的权比。实际算例表明,采用这些融合方法能明显地提高多类大地测量数据处理的结果精度。     

基于条件方程的方差分量估计公式

著名的Helmert方差分量估计公式是基于间接观测平差模型导出的。基于条件观测平差模型导出了方差分量估计公式并给出了实际应用范例,且对两种模型的方差分量估计公式的等价性进行了理论证明。算例表明,文中的估计公式能正确地估计出各类观测值的方差因子。     

抗差Helmert方差分量估计及其应用

本文讨论了当观测值中含有粗差时对Helmert方差分量估计结果的影响，为减弱这种影响，提出了抗差Helmert方差分量估计。试算结果表明，抗差Helmert方差分量估计具有一定的抵御粗差污染的能力，可用于具有多类或多种精度观测值的一并平差问题中。     

基于验后方差估计的稳健整体最小二乘方法

根据整体最小二乘的验后方差估计,求出观测值的验后方差,通过方差检验可找出方差异常大的观测值。然后根据经典权与观测值方差成反比的定义赋予它一个相应小的权进行下一步迭代平差,逐步实现粗差定位。通过坐标转换实验,利用一般最小二乘法(LS)、加权整体最小二乘法(WTLS)以及文中提出的稳健整体最小二乘法(RTLS)分别对待估参数进行求解对比,解算结果表明文中提出的方法能对粗差进行有效的定位,且估计量受粗差影响较小,具有稳健性,估算效果优于其它两种方法。     

L_p平差解的性质及抗差估计

本文首先分析了L_p平差的统计意义,证明了当观测误差服从p-范分布时,参数的极大似然估计即为L_p解。同时讨论了L_p的迭代解法及收敛性,给出了用改进的线性规划求L_1、L_∞解的方法。证明了L_p迭代解及L_1、L_∞严密解都是参数的无偏估计,同时构造了与L_p平差P值无关的单位权方差的无偏估计公式,并对L_p平差的效率作了讨论。最后分析了L_p平差与抗差估计的关系,给出了一种基于L_1解的抗差估计方法。     

自由设站法在地铁竣工测量中的应用研究

基于稳健抗差估计和方差分量估计理论,推导出结合胡贝尔法的Helmert三维自由设站间接平差模型.以地铁轨道某一断面数据作为应用对象,通过计算和分析,表明该平差模型不仅能够合理确定三类不同观测值的权值,而且可以抵抗已知点的坐标粗差对解算结果的影响,是一种切实可行的设站点解算方法,可为类似工程项目提供参考.     

抗差方差分量估计在GPS/GLONASS组合导航中的应用

运用GPS/GLONASS组合导航时,由于卫星系统的测距精度不同,需要在组合解算中采用方差分量估计进行合理定权和平差计算。但是,当GPS和GLONASS观测值中含有粗差时,若采用最小二乘方差分量估计则无法抵抗粗差的影响,从而降低平差结果的精度和可靠性。为了提高GPS/GLONASS组合解算的抗差性,本文引入了基于IGGⅢ等价权函数的抗差估计方法,经过对GPS/GLONASS实测数据的计算和分析,证明这是一种可行的和有效的方法。     

Robust estimation for correlated observations: two local sensitivity-based downweighting strategies

An iterative robust estimation procedure for correlated observations is proposed, in which the a-prior correlation coefficient matrix is not updated to alleviate the computational burden. Selection of the downweighting strategy plays a key role in the proposed method. Two local sensitivity-based strategies, one is based on the uniformly most powerful test statistics, the other is based on the standardized least squares residuals, are developed and analyzed. Monte Carlo simulations in the GPS network adjustment scenario demonstrate that, the two strategies can provide a certain resistance against the deteriorating effect of outlying observations on the parameter estimates; the former downweighting strategy is superior to the latter one, both in terms of robustness and computational efficiency.     

Improvement of parameter accuracy by choice and quality of observation

In a least squares adjustment (a minimum variance solution) using the technique of variation of coordinates (observation equations), a key result is the co-variance (dispersion) matrix of parameters. Assuming that standard errors of observations are used in the formation of the normal equations, rather than relative weights, this dispersion matrix gives the estimates of standard errors for the parameters solved for in the adjustment. A method will be presented which allows the designer of the observing plan to alter this dispersion matrix, which may not meet user requirements, so that it will meet user requirements and, from its inverse, solve mathematically for the selection and quality (accuracy) of the observations required to form this altered dispersion matrix of parameters.     

Regularization of geopotential determination from satellite data by variance components

 Different types of present or future satellite data have to be combined by applying appropriate weighting for the determination of the gravity field of the Earth, for instance GPS observations for CHAMP with satellite to satellite tracking for the coming mission GRACE as well as gradiometer measurements for GOCE. In addition, the estimate of the geopotential has to be smoothed or regularized because of the inversion problem. It is proposed to solve these two tasks by Bayesian inference on variance components. The estimates of the variance components are computed by a stochastic estimator of the traces of matrices connected with the inverse of the matrix of normal equations, thus leading to a new method for determining variance components for large linear systems. The posterior density function for the variance components, weighting factors and regularization parameters are given in order to compute the confidence intervals for these quantities. Test computations with simulated gradiometer observations for GOCE and satellite to satellite tracking for GRACE show the validity of the approach. Received: 5 June 2001 / Accepted: 28 November 2001     

GIS中地籍宗地面积的方差分量估计

探讨在地籍宗地数字化的面积处理问题中,把已知面积数据和数字化坐标同时作为观测值的面积处理方法.采用方差分量估计,解决已知面积数据参与平差时的权的确定.通过实例分析,认为采用条件平差方差分量估计方法来对宗地面积处理,能够更加合理地调整已有面积与由数字化坐标得到的面积值之间的差异.     

Adaptive collocation with application in height system transformation

In collocation applications, the prior covariance matrices or weight matrices between the signals and the observations should be consistent to their uncertainties; otherwise, the solution of collocation will be distorted. To balance the covariance matrices of the signals and the observations, a new adaptive collocation estimator is thus derived in which the corresponding adaptive factor is constructed by the ratio of the variance components of the signals and the observations. A maximum likelihood estimator of the variance components is thus derived based on the collocation functional model and stochastic model. A simplified Helmert type estimator of the variance components for the collocation is also introduced and compared to the derived maximum likelihood type estimator. Reasonable and consistent covariance matrices of the signals and the observations are arrived through the adjustment of the adaptive factor. The new adaptive collocation with related adaptive factor constructed by the derived variance components is applied in a transformation between the geodetic height derived by GPS and orthometric height. It is shown that the adaptive collocation is not only simple in calculation but also effective in balancing the contribution of observations and the signals in the collocation model.     

随机模型对解算西安流动SLR站坐标的影响

合理的参数估计及精度评定不仅需要可靠的函数模型,而且需要正确的随机模型。从权函数和粗差编辑两方面,研究了不同随机模型对西安流动卫星激光测距（satellite laser ranging,SLR）站坐标解算的影响,采用全球Lageos-1卫星观测数据计算了西安流动SLR站坐标。计算结果表明:①西安流动SLR站的观测精度和坐标解算精度均达到厘米级。②随机模型直接影响SLR站坐标的解算结果及可靠性;对于相同的计算弧段,抗差方差分量估计得到的站坐标精度最高、结果最稳定,残差加权均方差最小,观测资料利用率也最高;对于相同的计算方案,采用的SLR数据越多,坐标估计精度越高。     

粗差探测与识别统计检验量的比较分析

LS估计对粗差非常敏感。在粗差的探测与识别理论体系中,通常采用正态检验、学生氏t检验以及τ检验等,笔者对此进行比较分析。标准化局部敏感度指标与标准化LS残差均可用来作正态检验,但研究表明,当观测量相关时,前者的检验功效大于后者。先验单位权方差因子未知时,可依据内部学生化残差及外部学生化残差分别进行τ检验和学生氏t检验。...     

Numerical prediction methods for clock deviation based on two-way satellite time and frequency transfer data

Three functional models, polynomial, spectral analysis, and modified AR model, are studied and compared in fitting and predicting clock deviation based on the data sequence derived from two-way satellite time and frequency transfer. A robust equivalent weight is applied, which controls the significant influence of outlying observations. Some conclusions show that the prediction precision of robust estimation is better than that of LS. The prediction precision calculated from smoothed observations is higher than that calculated from sampling observations. As a count of the obvious period variations in the clock deviation sequence, the predicted values of polynomial model are implausible. The prediction precision of spectral analysis model is very low, but the principal periods can be determined. The prediction RMS of 6-hour extrapolation interval is 1 ns or so, when modified AR model is used.     

基于卫星双向时间频率传递进行钟差预报的方法研究

用多项式拟合、谱分析、改进的AR模型三种方法对由卫星双向时间频率传递得出的钟差时间序列进行了拟舍和预报分析。为了抵制钟差时间序列中异常值的影响，引入了“抗差等价权”，利用卫星双向时间频率传递得到的1d的钟差，按不问采样率、不同时间跨度进行计算分析。结果表明，抗差估计的预报精度明显高于最小二乘估计；平滑值的预报精度高于采样值；由于钟差时间序列中有明显的周期变化，多项式进行钟差预报的精度不可靠；用谱分析进行钟差预报的精度不高，但可以发现钟差时间序列中的主要周期变化；改进的AR模型预报精度最高，预报6h钟差的RMs在1ns左右。     

基于卫星双向时间频率传递进行钟差预报的几种方法

Three functional models, polynomial, spectral analysis, and modified AR model, are studied and compared in fitting and predicting clock deviation based on the data sequence derived from two-way satellite time and frequency transfer. A robust equivalent weight is applied, which controls the significant influence of outlying observations. Some conclusions show that the prediction precision of robust estimation is better than that of LS. The prediction precision calculated from smoothed observations is higher than that calculated from sampling observations. As a count of the obvious period variations in the clock deviation sequence, the predicted values of polynomial model are implausible. The prediction precision of spectral analysis model is very low, but the principal periods can be determined. The prediction RMS of 6-hour extrapolation interval is Ins or so, when modified AR model is used.