Robust estimation of geodetic datum transformation

The robust estimation of geodetic datum transformation is discussed. The basic principle of robust estimation is introduced. The error influence functions of the robust estimators, together with those of least-squares estimators, are given. Particular attention is given to the robust initial estimates of the transformation parameters, which should have a high breakdown point in order to provide reliable residuals for the following estimation. The median method is applied to solve for robust initial estimates of transformation parameters since it has the highest breakdown point. A smooth weight function is then used to improve the efficiency of the parameter estimates in successive iterative computations. A numerical example is given on a datum transformation between a global positioning system network and the corresponding geodetic network in China. The results show that when the coordinates are contaminated by outliers, the proposed method can still give reasonable results. Received: 25 September 1997 / Accepted: 1 March 1999     

Robust estimator for correlated observations based on bifactor equivalent weights

 A new robust parameter estimator for the adjustment of correlated observations is developed based on a `bifactor reduction' model of weight elements. A shrinking factor for weight elements is proposed. The new equivalent weight matrix composed by the bifactor weight elements preserves the symmetry and keeps the original correlation coefficients unchanged. The new parameter estimator with its error influence function is derived. The robustness and efficiency of the new robust estimator is demonstrated with a simulated example and some conclusions are drawn. Received: 5 March 2001 / Accepted: 17 January 2002     

抗差加权整体最小二乘模型的牛顿-高斯算法

基于加权整体最小二乘的牛顿-高斯迭代算法,提出了一种抗差加权整体最小二乘模型。利用标准化残差构造权因子函数,并采用中位数法获得具有抗差性的单位权中误差估值,能同时实现观测空间和结构空间抗差。为获得标准化残差,利用线性近似的协因数传播律推导了加权整体最小二乘残差协因数阵的表达式,并给出模型的迭代计算方法。试验结果表明:对于加权整体最小二乘的粗差处理问题,本文提出的方法具有良好的抗差性能,参数估值与不含粗差时加权整体最小二乘的结果没有显著的差异,性能优于直接由残差构造的稳健加权整体最小二乘模型。     

New results in airborne vector gravimetry using strapdown INS/DGPS

A method for airborne vector gravimetry has been developed. The method is based on developing the error dynamics equations of the INS in the inertial frame where the INS system errors are estimated in a wave estimator using inertial GPS position as update. Then using the error-corrected INS acceleration and the GPS acceleration in the inertial frame, the gravity disturbance vector is extracted. In the paper, the focus is on the improvement of accuracy for the horizontal components of the airborne gravity vector. This is achieved by using a decoupled model in the wave estimator and decorrelating the gravity disturbance from the INS system errors through the estimation process. The results of this method on the real strapdown INS/DGPS data are promising. The internal accuracy of the horizontal components of the estimated gravity disturbance for repeated airborne lines is comparable with the accuracy of the down component and is about 4–8 mGal. Better accuracy (2–4 mGal) is achieved after applying a wave-number correlation filter (WCF) to the parallel lines of the estimated airborne gravity disturbances.     

A new approach for airborne vector gravimetry using GPS/INS

A new method for airborne vector gravimetry using GPS/INS has been developed and the results are presented. The new algorithm uses kinematic accelerations as updates instead of positions or velocities, and all calculations are performed in the inertial frame. Therefore, it is conceptually simpler, easier, more straightforward and computationally less expensive compared to the traditional approach in which the complex navigation equations should be integrated. Moreover, it is a unified approach for determining all three vector components, and no stochastic gravity modeling is required. This approach is based on analyzing the residuals from the Kalman filter of sensor errors, and further processing with wavenumber coefficient filterings is applied in case closely parallel tracks of data are available. An application to actual test-flight data is performed to test the validity of the new algorithm. The results yield an accuracy in the down component of about 3–4 mGal. Also, comparable results are obtained for the horizontal components with accuracies of about 6 mGal. The gravity modeling issue is discussed and alternative methods are presented, none of which improves on the original approach. Received: 18 April 2000 / Accepted: 14 August 2000     

Regularization of gravity field estimation from satellite gravity gradients

 The performance of the L-curve criterion and of the generalized cross-validation (GCV) method for the Tikhonov regularization of the ill-conditioned normal equations associated with the determination of the gravity field from satellite gravity gradiometry is investigated. Special attention is devoted to the computation of the corner point of the L-curve, to the numerically efficient computation of the trace term in the GCV target function, and to the choice of the norm of the residuals, which is important for the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) in the presence of colored observation noise. The trace term in the GCV target function is estimated using an unbiased minimum-variance stochastic estimator. The performance analysis is based on a simulation of gravity gradients along a 60-day repeat circular orbit and a gravity field recovery complete up to degree and order 300. Randomized GCV yields the optimal regularization parameter in all the simulations if the colored noise is properly taken into account. Moreover, it seems to be quite robust against the choice of the norm of the residuals. It performs much better than the L-curve criterion, which always yields over-smooth solutions. The numerical costs for randomized GCV are limited provided that a reasonable first guess of the regularization parameter can be found. Received: 17 May 2001 / Accepted: 17 January 2002     

A bound for the Euclidean norm of the difference between the best linear unbiased estimator and a linear unbiased estimator

 A bound is established for the Euclidean norm of the difference between the best linear unbiased estimator and any linear unbiased estimator in the general linear model. The bound involves the spectral norm of the difference between the dispersion matrices of the two estimators, and the residual sum of squares, all evaluated at the assumed model, but is independent of the provenance of the observation vector at hand. The bound, a straightforward consequence of first principles in Gauss–Markov theory, generalizes previous results on the difference between the best linear unbiased estimator and the ordinary least-squares estimator. In a numerical example from repeated precise levelling, the bound is used to analyse the sensitivity of estimates of vertical motion to the choice of estimator. Received: 9 September 1999 / Accepted: 15 March 2002     

Possible improvement of Earth orientation forecast using autocovariance prediction procedures

 Autocovariance prediction has been applied to attempt to improve polar motion and UT1-UTC predictions. The predicted polar motion is the sum of the least-squares extrapolation model based on the Chandler circle, annual and semiannual ellipses, and a bias fit to the past 3 years of observations and the autocovariance prediction of these extrapolation residuals computed after subtraction of this model from pole coordinate data. This prediction method has been applied also to the UT1-UTC data, from which all known predictable effects were removed, but the prediction error has not been reduced with respect to the error of the current prediction model. However, the results show the possibility of decreasing polar motion prediction errors by about 50 for different prediction lengths from 50 to 200 days with respect to the errors of the current prediction model. Because of irregular variations in polar motion and UT1-UTC, the accuracy of the autocovariance prediction does depend on the epoch of the prediction. To explain irregular variations in x, y pole coordinate data, time-variable spectra of the equatorial components of the effective atmospheric angular momentum, determined by the National Center for Environmental Prediction, were computed. These time-variable spectra maxima for oscillations with periods of 100–140 days, which occurred in 1985, 1988, and 1990 could be responsible for excitation of the irregular short-period variations in pole coordinate data. Additionally, time-variable coherence between geodetic and atmospheric excitation function was computed, and the coherence maxima coincide also with the greatest irregular variations in polar motion extrapolation residuals. Received: 22 October 1996 / Accepted: 16 September 1997     

Adaptive fitting of systematic errors in navigation

To use Kalman filtering for kinematic positioning and navigation, we have to deal with both observational and kinematic models. Both of the functional models may contain global or local systematic errors. The influence functions of the systematic errors on the estimates of kinematic states are derived. An adaptive fitting method for systematic errors of the observations and kinematic model errors is presented. The systematic errors are fitted with a mean or a weighted mean by using the residuals of observations and residuals of predicted states within a chosen time window. The covariance matrices of the modified observations and the predicted states are estimated within the same window. The estimation formulae and calculation strategy, as well as a real example, are given. It is shown by theory and calculations that Kalman filtering based on the adaptive fittings of the systematic errors and covariance matrices can, to some degree, resist the influences of systematic errors on the estimated states of navigation.     

Data weighting and solution assessment in combination

A data weighting method for combining different geodetic data based on statistical tests of the residuals is discussed. By requiring the residuals of combination solution to be white Gaussian as a sample of random noise, the relative weight factors can be determined internally, independent of external comparisons. To achieve this goal and to assess the solution, the Pearson χ² and higher-order moments of residuals are used to measure quantitatively the discrepancies between the residuals and white Gaussian noise. Received: 30 June 1998 / Accepted: 19 April 1999     

Modelling position dependent errors in gravimetric deflections of the vertical

Comparisons of gravimetric and astrogeodetic deflections of the vertical in the Australian region indicate that the former are affected by position dependent systematic errors, even after orientation onto the Australian Geodetic Datum. These are probably due to errors in the predicted mean anomalies for gravimetrically unsurveyed oceanic regions to the east, south and west of the continent. Deflection component residuals (astrogeodetic minus oriented gravimetric) at 83 control stations are made the observables in a set of observation equations, based on the Vening Meinesz equations, from which pseudocorrections to the mean anomalies for a set of arbitrarily selected surface elements are computed. These pseudocorrections compensate for prediction errors in much larger unsurveyed regions. Their effects on individual deflection components are calculated using the Vening Meinesz equations. Statistical tests indicate that pseudocorrections computed for four large offshore elements and six smaller elements in unsurveyed areas produce corrections to the gravimetric deflections which make the ξ and η components in seconds of arc consistent with normally distributed populations N (0.00, 0.70²).     

一种Kalman滤波系统误差及其协方差矩阵的半参数估计方法

提出用半参数估计理论来解决系统误差对Kalman滤波解的影响问题。即用半参数模型中的非参数分量表达观测模型和动力学模型中未知的系统误差,在移动的窗口内,基于观测残差和状态向量预测残差拟合模型系统误差,进而修正相应的观测向量和状态预测向量的协方差矩阵,以消除系统误差对滤波的影响。同时这种方法还有明显的优点,就是在滤波过程中不需要对系统误差做任何假设。文中推导了基于正则核估计来解算导航系统半参数模型的相应公式,并根据一个模拟的算例,证明了算法的有效性。     

随机游动模型下系统误差补偿算法研究

采用地球同步静止卫星的观测数据,通过批处理方法及EM算法分别加以处理。利用偏度、峰度正态性检验方法对批处理方法所得的残差进行正态性检验,鑫诺卫星观测数据中不仅含有偶然误差,还存在系统误差等,批处理方法在处理含有系统误差的观测数据时所得的结果不可靠。相对于批处理方法,利用随机游动模型描述系统误差,基于数字滤波方法的EM算法在处理含有系统误差数据时具有可行性,在一定程度上达到了提高数据处理精度的目的。     

GPS sidereal filtering: coordinate- and carrier-phase-level strategies

Multipath error is considered one of the major errors affecting GPS observations. One can benefit from the repetition of satellite geometry approximately every sidereal day, and apply filtering to help minimize this error. For GPS data at 1 s interval processed using a double-difference strategy, using the day-to-day coordinate or carrier-phase residual autocorrelation determined with a 10-h window leads to the steadiest estimates of the error-repeat lag, although a window as short as 2 h can produce an acceptable value with > 97% of the optimal lag’s correlation. We conclude that although the lag may vary with time, such variation is marginal and there is little advantage in using a satellite-specific or other time-varying lag in double-difference processing. We filter the GPS data either by stacking a number of days of processed coordinate residuals using the optimum “sidereal” lag (23 h 55 m 54 s), and removing these stacked residuals from the day in question (coordinate space), or by a similar method using double-difference carrier-phase residuals (observational space). Either method results in more consistent and homogeneous set of coordinates throughout the dataset compared with unfiltered processing. Coordinate stacking reduces geometry-related repeating errors (mainly multipath) better than carrier-phase residual stacking, although the latter takes less processing time to achieve final filtered coordinates. Thus, the optimal stacking method will depend on whether coordinate precision or computational time is the over-riding criterion.     

导航解算中的系统误差及其协方差矩阵拟合

利用Kslman滤波进行导航定位计算不得不涉及观测函数模型和动力学模型,而观测函数模型和动力学模型经常含有系统误差或区域性系统误差.本文提出了一种基于移动窗口的函数模型和随机模型系统误差自适应拟合法.基于相同的窗口给出了相应的观测向量和状态预测向量的协方差矩阵估计方法,其协方差矩阵的估计与现有的Sage滤波不同.利用经系统误差修正后的观测向量和状态预测向量及相应的协方差矩阵,再进行动态导航滤波计算,能有效提高导航解的精度.文中给出了开窗估计系统误差的公式,并利用实测数据验证了该算法的可行性和实用性.计算结果表明该算法能有效地抵制系统误差对导航滤波结果的影响.     

The northern European geoid: a case study on long-wavelength geoid errors

 The long-wavelength geoid errors on large-scale geoid solutions, and the use of modified kernels to mitigate these effects, are studied. The geoid around the Nordic area, from Greenland to the Ural mountains, is considered. The effect of including additional gravity data around the Nordic/Baltic land area, originating from both marine, satellite and ground-based measurements, is studied. It is found that additional data appear to increase the noise level in computations, indicating the presence of systematic errors. Therefore, the Wong–Gore modification to the Stokes kernel is applied. This method of removing lower-order terms in the Stokes kernel appears to improve the geoid. The best fit to the global positioning system (GPS) leveling points is obtained with a degree of modification of approximately 30. In addition to the study of modification errors, the results of different methods of combining satellite altimetry gravity and other gravimetry are presented. They all gave comparable results, at the 6-cm level, when evaluated for the Nordic GPS networks. One dimensional (1-D) and 2-D fast Fourier transform (FFT) methods are also compared. It is shown that even though methods differ by up to 6 cm, the fit to the GPS is essentially the same. A surprising conclusion is that the addition of more data does not always produce a better geoid, illustrating the danger of systematic errors in data. Received: 4 July 2001 / Accepted: 21 February 2002     

基于经验模态分解的GPS基线解算模型

非建模系统误差是影响高精度GPS基线解算精度的一个重要因素,文章给出基于经验模态分解的GPS基线解算模型,有效消弱系统误差对基线解的影响。在现有经验模态分解理论的基础上,定义经验模态分解的多尺度分解与重构结构,并由此给出基于经验模态的系统趋势分离模型,依据累积标准化模量的均值随尺度的变化确定系统误差与噪声分离尺度的选择标准。给出基于经验模态分解的GPS基线解算的技术路线,首先计算GPS相位双差观测方程的浮点解残差序列,分离出系统误差并用于修正GPS双差观测值,重新计算双差浮点解,采用Lambda算法固定整周模糊度,计算固定基线解,从而消弱系统误差对基线解算的影响,提高基线固定解的可靠性。并采用实测GPS数据验证模型,F-ratio指数与W-ratio指数表明系统误差消弱后,基线固定解可靠性得到明显提高,重新计算的残差序列表明系统误差得到很好的消弱。     

The Bayesian approach applied to GPS ambiguity resolution. A mixture model for the discrete–real ambiguities alternative

 The problem of phase ambiguity resolution in global positioning system (GPS) theory is considered. The Bayesian approach is applied to this problem and, using Monte Carlo simulation to search over the integer candidates, a practical expression for the Bayesian estimator is obtained. The analysis of the integer grid points inside the search ellipsoid and their evolution with time, while measurements are accumulated, leads to the development of a Bayesian theory based on a mathematical mixture model for the ambiguity. Received: 29 March 2001 / Accepted: 3 September 2001     

Analytical solar radiation pressure modelling for GLONASS using a pixel array

 A new method for calculating analytical solar radiation pressure models for GNSS spacecraft has been developed. The method simulates the flux of light from the Sun using a pixel array. The method can cope with a high level of complexity in the spacecraft structure and models effects due to reflected light. Models have been calculated and tested for the Russhar global navigation satellite system GLONASS IIv spacecraft. Results are presented using numerical integration of the force model and long-arc satellite laser ranging (SLR) analysis. The integrated trajectory differs from a precise orbit calculated using a network of global tracking stations by circa 2 m root mean square over a 160 000-km arc. The observed − computed residuals for the 400-day SLR arc are circa 28 mm. Received: 23 December 1999 / Accepted: 28 August 2000     

具有稳健初值的选权迭代法

提出先采用线性规划来确定残差的初值，然后再进行选权迭代这样一种方法，其估计结果既具有线性规划的稳健性，又具有最小二乘的最优性。试验表明，这种基于线性规划的稳健估计具有很强的稳健性和检测粗差的能力，其计算结果与没有粗差时的最小二乘估计结果一致，且方法简单、可靠、实用。