卫星重力梯度数据解算位系数的最小二乘配置法

卫星重力梯度测量在恢复地球重力场的研究中已经得到了广泛应用。本文通过空间扰动位协方差函数特性,得出卫星重力梯度数据与引力位系数的相关协方差函数。利用最小二乘配置法,最终推导出由重力梯度数据直接解算引力位系数的函数表达式,并简要分析其实用性。     

GOCE卫星测量恢复地球重力场模型的理论与方法

研究了GOCE卫星测量恢复地球重力场模型的理论与方法。论文的主要工作和创新点有： (1) 建立了扰动重力梯度张量各分量没有奇异性的详细计算模型,解决了重力梯度张量Txx分量在两极地区计算的奇异性难题。 (2) 系统研究了卫星重力梯度数据向下延拓的解析法、泊松积分迭代法和卫星重力梯度数据格网化的移动平均法、反距离加权法、普通克里金法,建立了相应的数学模型,导出了相应的计算公式,并采用“直接法”和“移去-恢复法”两种方案对其向下延拓和格网化效果进行了测试。 (3) 分析了能量守恒方程中各项误差对沿轨扰动位计算结果的影响,建立了利用GOCE模拟数据确定地球重力场的最小二乘直接法、调和分析法、最小二乘配置法的实用数学模型,并做了大量的模拟计算。 (4) 建立了利用扰动引力梯度张量各单分量和组合分量确定地球重力场的最小二乘直接法去奇异性计算模型;推导了利用扰动引力梯度张量单分量和组合分量解算地球重力场的调和分析法模型;进一步推导了扰动引力梯度张量各个分量之间的自协方差和互协方差函数及其与引力位系数之间协方差函数的具体计算公式。 (5) 推导了利用不同类型重力测量数据确定地球重力场的联合平差法数学模型,介绍并分析了模型中各类数据最优定权的参数协方差法和方差分量估计法。 (6) 论述了谱组合法的基本原理,给出了多种类型重力测量数据联合处理的谱权及谱组合的通用表达式,基于调和分析方法推导了SST+SGG、SST+SGG＋Δg和SST+SGG＋Δg+N恢复地球重力场模型的谱组合公式及对应谱权的具体形式。 (7) 推导了利用迭代法联合不同类型重力测量数据反演地球重力场模型的基本原理公式,并给出了其具体实现步骤。 (8) 分析并计算了重力卫星轨道高度、卫星星间距离和卫星轨道倾角的设计指标;讨论了双星轨道长半轴的一致性要求、双星姿态俯仰角的控制要求以及双星编队保持机动的时间间隔要求。 (9) 确定了KBR系统的星间距离、星间距离变化率和星间加速度的精度指标;设计了星载GPS系统的卫星轨道位置和速度以及加速度计测量的精度指标;计算了加速度计检验质量质心到卫星质心的调整距离精度指标;分析了恒星敏感器的姿态角测量精度和稳定度;计算了参考重力场模型对于累计大地水准面精度和积分卫星轨道的影响。 (10) 研制了一套利用卫星重力测量数据反演地球重力场模型的软件平台,可对卫星重力测量数据处理及其精度评估提供一些基本方法,并为我国卫星重力测量系统的总体战技指标和主要有效载荷技术指标的量化分析、论证提供理论和技术支持,为我国未来的卫星重力测量系统提供可能的积累和参考。     

总体最小二乘平差理论及其在测绘数据处理中的应用

最小二乘法是测量数据处理的最基本、应用最广泛的方法,对于经典的最小二乘法是只考虑观测向量的误差,假设系数阵没有误差或不考虑系数阵的误差。然而系数矩阵包含误差的情况在测量数据实践中是存在的。总体最小二乘法旨在解决顾及系数矩阵误差的一种数据     

基于变量投影法的自回归模型方差分量估计

在自回归(autoregressive, AR)模型中,系数矩阵与观测向量中的随机误差同源。针对AR模型平差时观测权阵分配不合理、随机模型不准确的情况,采用变量投影法提取系数矩阵和观测向量构成的增广矩阵中的随机量,将变量误差(errors-in-variables,EIV)模型转化为非线性高斯-赫尔默特(Gauss-Helmert,GH)模型,利用非线性最小二乘理论得到一种结构总体最小二乘(structural total least squares, STLS)算法,并与最小二乘方差分量估计(least squares variance component estimation, LS-VCE)相结合推导出STLS问题的一种方差分量估计算法,将其应用到AR模型的方差分量估计。通过实测算例对算法有效性进行了验证,取得了与已有方法一致的结果。该算法观测权阵的构造十分简洁,同时也可用于协方差分量的估计。     

总体最小二乘用于线阵卫星遥感影像光束法平差解算

顾及像点观测方程的系数矩阵中存在随机误差,提出了基于总体最小二乘的线阵卫星遥感影像光束法平差模型。在假定像点观测误差和系数矩阵误差均为独立、等精度分布的基础上,利用拉格朗日条件极值法推导了包含外方位元素虚拟观测方程和控制点误差方程的总体最小二乘光束法平差算法的具体公式和计算方法。该方法利用方差分量估计确定各类虚拟观测值的方差,可求解包含多类虚拟观测量的平差问题,并可用先验信息或岭迹法确定系数矩阵观测值的权比例系数,从而克服了现有总体最小二乘虚拟观测方法不能处理多类虚拟观测值的不足,确保了光束法平差可正确有效求解。分别利用模拟算例与两组真实影像进行了试验验证。结果表明,相比于常规最小二乘虚拟观测法以及现有总体最小二乘虚拟观测方法,本文方法具有更高的求解精度与适应性。相较于传统线阵卫星遥感影像光束法平差方法,本文方法可以获得更高的平差计算精度。     

多元总体最小二乘问题的牛顿解法

为提高多元总体最小二乘问题参数估值的解算效率,推导了基于牛顿法的多元加权总体最小二乘算法;分析比较了基于牛顿法的多元加权总体最小二乘解和基于拉格朗日乘数法多元加权总体最小二乘解之间的关系,根据协因数传播律给出了多元总体最小二乘平差的16种协因数阵的近似计算公式。新算法能够解决观测矩阵和系数矩阵元素具有相关性的问题,并且可以把观测矩阵和系数矩阵的随机元素和常数元素纳入到一个协因数阵中进行处理。算例结果表明,本文提出的多元总体最小二乘问题的牛顿解法可行且收敛速度更快。     

一种改进的计算中央区扰动引力梯度对角线分量方法

针对利用Stokes公式计算邻近地面点扰动引力梯度时,径向分量的计算出现奇异性,积分中央区对水平分量的忽略导致扰动引力梯度对角线三分量之和不满足拉普拉斯方程的现象,引入了双二次多项式插值和变量替换技术,推导了扰动引力梯度对角线三分量在中央区的改进计算方法.分别利用仿真数据和真实数据开展了计算验证工作,并将计算结果与泰勒...     

基于方差分量估计的正则化配置法及其在多源重力数据融合中的应用

针对信号与误差的方差分量不一致问题及协方差阵病态性问题,分别在多源重力数据最小二乘配置融合过程中引入方差分量估计方法及Tikhonov正则化方法,得到基于方差分量估计的正则化配置法,实际算例结果表明,利用该方法能够有效削弱上述问题,减小重力数据融合结果的系统差,提高数据融合的精度及可靠性。     

加权总体最小二乘平差的改进算法

当观测向量和系数矩阵不等精度时,利用系数矩阵元素和观测向量之间的映射关系,通过误差传播定律推导了系数矩阵的协因数阵,算例结果表明,改进的加权总体最小二乘法能够得到正确、合理的参数,且本文方法简单、实用。     

确定大地水准面的Tikhonov最小二乘配置法

LSC法（最小二乘配置法）因能融合不同种类重力观测数据确定大地水准面的特性而受到广泛关注,但由于协方差矩阵存在病态性,微小的观测误差将被协方差矩阵的小奇异值放大,导致计算的配置结果不稳定且精度偏低。本文提出Tikhonov_LSC法,即在LSC法中引入Tikhonov正则化算法,基于GCV法选择协方差矩阵的正则化参数,利用正则化参数修正协方差矩阵的小奇异值,以抑制其对观测误差的放大影响。基于Tikhonov_LSC法计算大地水准面,能有效提高其稳定性和精度。通过以EGM2008重力场模型分别计算山区、丘陵和海域重力异常作为基础数据确定相应区域大地水准面的实验,验证了该方法的有效性。     

航空重力向下延拓的最小二乘配置Tikhonov正则化法

基于最小二乘配置法向下延拓航空重力的过程中,由于协方差矩阵严重病态,影响延拓结果的稳定性和精度。针对这一问题,提出了航空重力向下延拓的最小二乘配置Tikhonov正则化法。基于全球协方差函数模型建立航空重力数据与地面重力数据的协方差关系,引入基于广义交叉验证法,选择正则化参数的Tikhonov正则化法改善协方差矩阵的病态性,抑制观测噪声对延拓结果的放大影响。基于EGM2008重力场模型,设计了山区、丘陵和海域3种不同地形区域的航空重力数据向下延拓的仿真实验,实验结果验证了该方法的有效性。     

局部重力场最小二乘配置通用表示技术

在分析局部重力场最小二乘配置法技术特点的基础上，推导出一种能综合多种类型、不同高度重力场元经验协方差函数的通用表达方法，以期实现局部重力场元的内插、外推、延拓或其他不同高度的重力场元估计一体化。分析了最小二乘配置技术的一些性能以及算法实现中应注意的问题。     

Comparison of methods for marine gravity determination from satellite altimetry data in the Labrador Sea

One-year average satellite altimetry data from the Exact Repeat Missions (ERM) of GEOSAT have been used to determine marine gravity disturbances in the Labrador Sea region using the inverse Hotine approach with FFT techniques. The derived satellite gravity information has been compared to shipboard gravity as well as gravity information derived by least-squares collocation (LSC), GEMT3 and OSU91A geopotential models in the Orphan Knoll area. The RMS and mean differences between satellite and shipboard gravity disturbances are about 8.0 and 2.8 mGal, respectively. There is no significantly difference between the results obtained using FFT and LSC.     

Marine gravity and geoid determination by optimal combination of satellite altimetry and shipborne gravimetry data

. Satellite altimetry derived geoid heights and marine gravity anomalies can be combined to determine a detailed gravity field over the oceans using the least-squares collocation method and spectral combination techniques. Least-squares collocation, least-squares adjustment in the frequency domain and input-output system theory are employed to determine the gravity field (both geoid and anomalies) and its errors. This paper intercompares these three techniques using simulated data. Simulation studies show that best results are obtained by the input-output system theory among the three prediction methods. The least-squares collocation method gives results which are very close to but a little bit worse than those obtained using input-output system theory. This slightly poorer performance of the least-squares collocation method can be explained by the fact that it uses isotropic structured covariance (thus approximate signal PSD information) while the system theory method uses detailed signal PSD information. The method of least-squares adjustment in the frequency domain gives the poorest results among these three methods because it uses less information than the other two methods (it ignores the signal PSDs). The computations also show that the least-squares collocation and input-output system theory methods are not as sensitive to noise levels as the least-squares adjustment in the frequency domain method is. Received 19 January 1996; Accepted 17 July 1996     

The combination of GNSS-levelling data and gravimetric (quasi-) geoid heights in the presence of noise

We propose a methodology for the combination of a gravimetric (quasi-) geoid with GNSS-levelling data in the presence of noise with correlations and/or spatially varying noise variances. It comprises two steps: first, a gravimetric (quasi-) geoid is computed using the available gravity data, which, in a second step, is improved using ellipsoidal heights at benchmarks provided by GNSS once they have become available. The methodology is an alternative to the integrated processing of all available data using least-squares techniques or least-squares collocation. Unlike the corrector-surface approach, the pursued approach guarantees that the corrections applied to the gravimetric (quasi-) geoid are consistent with the gravity anomaly data set. The methodology is applied to a data set comprising 109 gravimetric quasi-geoid heights, ellipsoidal heights and normal heights at benchmarks in Switzerland. Each data set is complemented by a full noise covariance matrix. We show that when neglecting noise correlations and/or spatially varying noise variances, errors up to 10% of the differences between geometric and gravimetric quasi-geoid heights are introduced. This suggests that if high-quality ellipsoidal heights at benchmarks are available and are used to compute an improved (quasi-) geoid, noise covariance matrices referring to the same datum should be used in the data processing whenever they are available. We compare the methodology with the corrector-surface approach using various corrector surface models. We show that the commonly used corrector surfaces fail to model the more complicated spatial patterns of differences between geometric and gravimetric quasi-geoid heights present in the data set. More flexible parametric models such as radial basis function approximations or minimum-curvature harmonic splines perform better. We also compare the proposed method with generalized least-squares collocation, which comprises a deterministic trend model, a random signal component and a random correlated noise component. Trend model parameters and signal covariance function parameters are estimated iteratively from the data using non-linear least-squares techniques. We show that the performance of generalized least-squares collocation is better than the performance of corrector surfaces, but the differences with respect to the proposed method are still significant.     

扰动重力梯度的非奇异表示

在局部指北坐标系中用地心球坐标来表示扰动重力梯度张量,当计算点趋近于两极时,由于Legendre函数的一阶和二阶导数以及分母上所含余纬的正弦函数,将导致扰动重力梯度张量的计算出现无穷大。因此,本文引入了Legendre函数的一阶和二阶导数以及 无奇异性的计算公式,并且进一步推导了 无奇异性的计算公式。在将Legendre函数的一阶和二阶导数以及 、 无奇异性的计算公式代入到扰动重力梯度张量各分量的求解中时,又充分考虑了m等于0,1,2以及其它量时的复杂情况,建立了扰动重力梯度张量各分量无奇异性的详细计算模型。通过模拟实验表明,本文所建立的详细计算模型不仅能够完全满足当前卫星重力梯度张量计算的精度要求,而且模型稳定、可靠、易于编程实现。     

Gravity-field improvement in the Mediterranean Sea by estimating the bottom topography using collocation

The contribution of bathymetry to the prediction of quantities related to the gravity field (e.g., gravity anomalies, geoid heights) is discussed in an extended test area of the central Mediterranean Sea. Sea gravity anomalies and a priori statistical characteristics of depths are used in a least-squares collocation procedure in order to produce new depths, giving a better smoothing of the gravity field when using a remove-restore procedure. The effect of the bottom topography on gravity-field modeling is studied using both the original and the new depths through a residual terrain modeling reduction. The numerical tests show a considerable smoothing of the sea gravity anomalies and the available altimeter heights when the new depth information is taken into account according to the covariance analysis performed. Moreover, geoid heights are computed by combining the sea gravity anomalies either with the original depths or with the new ones, using as a reference surface the OSU91A geopotential model. Comparing the computed geoid heights with adjusted altimeter sea-surface heights (SSHs), better results are obtained when subtracting the attraction of the new depth information. Similar results are obtained when predicting gravity anomalies from altimeter SSHs where the terrain effect on altimetry is based on the new bottom topography. Received: 10 September 1996 / Accepted: 4 August 1997     

Airborne LaCoste &; Romberg gravimetry: a space domain approach

This paper introduces a new approach to reduce the airborne gravity data acquired by a LaCoste &; Romberg (L&;R) air/sea gravimeter, or other similar gravimeters. The acceleration exerted on the gravimeter is the sum of gravity and the vertical and Eötvös accelerations of the aircraft. The L&;R gravimeter outputs are: (1) the beam position, (2) the spring tension and (3) the cross coupling. Vertical and Eötvös accelerations are computed from GPS-derived aircraft positions. However, the vertical perturbing acceleration sensed by the gravimeter is not the same as the one sensed by the aircraft (via GPS). A determination of the aircraft-to-sensor transfer function is necessary. The second-order differential equation of the motion of the gravimeter’s beam mixes all the input and output parameters of the gravimeter. Conventionally, low-pass filtering in the frequency domain is used to extract the gravity signal, the filter being applied to each flight-line individually. By transforming the differential equation into an integral equation and by introducing related covariance matrices, we develop a new filtering method based on a least-squares approach that is able to take into account, in one stage, the data corresponding to all flight-lines. The a posteriori covariance matrix of the estimated gravity signal is an internal criterion of the precision of the method. As an example, we estimate the gravity values along the flight-lines from an airborne gravity survey over the Alps and introduce an a priori covariance matrix of the gravity disturbances from a global geopotential model. This matrix is used to regularize the ill-posed Fredholm integral equation introduced in this paper.     

Local characteristics of the gravity anomaly covariance function

Summary Using a data set of 260 000 gravity anomalies it is shown that common characteristics for a local covariance function exist in an area as large as Canada excluding the Rocky Mountains. After eliminating global features by referencing the data to the GEM-10 satellite solution, the shape of the covariance function is remarkably consistent from one sample area to the next. The determination of the essential parameters and the fitting of the covariance function are discussed in detail. To test the reliability of the derived function, deflections of the vertical are estimated at about 230 stations where astrogeodetic data are available. Results show that the standard error obtained from the discrepancies is about1″ for each component and that the error covariance matrix of least-squares collocation reflects this accuracy remarkably well.     

Methodology and use of tensor invariants for satellite gravity gradiometry

Although its use is widespread in several other scientific disciplines, the theory of tensor invariants is only marginally adopted in gravity field modeling. We aim to close this gap by developing and applying the invariants approach for geopotential recovery. Gravitational tensor invariants are deduced from products of second-order derivatives of the gravitational potential. The benefit of the method presented arises from its independence of the gradiometer instrument’s orientation in space. Thus, we refrain from the classical methods for satellite gravity gradiometry analysis, i.e., in terms of individual gravity gradients, in favor of the alternative invariants approach. The invariants approach requires a tailored processing strategy. Firstly, the non-linear functionals with regard to the potential series expansion in spherical harmonics necessitates the linearization and iterative solution of the resulting least-squares problem. From the computational point of view, efficient linearization by means of perturbation theory has been adopted. It only requires the computation of reference gravity gradients. Secondly, the deduced pseudo-observations are composed of all the gravitational tensor elements, all of which require a comparable level of accuracy. Additionally, implementation of the invariants method for large data sets is a challenging task. We show the fundamentals of tensor invariants theory adapted to satellite gradiometry. With regard to the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite gradiometry mission, we demonstrate that the iterative parameter estimation process converges within only two iterations. Additionally, for the GOCE configuration, we show the invariants approach to be insensitive to the synthesis of unobserved gravity gradients.