Groebner-basis solution of the three-dimensional resection problem (P4P)

The three-dimensional (3-D) resection problem is usually solved by first obtaining the distances connecting the unknown point P{X,Y,Z} to the known points P_i{X_i,Y_i,Z_i}i=1,2,3 through the solution of the three nonlinear Grunert equations and then using the obtained distances to determine the position {X,Y,Z} and the 3-D orientation parameters {,, }. Starting from the work of the German J. A. Grunert (1841), the Grunert equations have been solved in several substitutional steps and the desire as evidenced by several publications has been to reduce these number of steps. Similarly, the 3-D ranging step for position determination which follows the distance determination step involves the solution of three nonlinear ranging (`Bogenschnitt') equations solved in several substitution steps. It is illustrated how the algebraic technique of Groebner basis solves explicitly the nonlinear Grunert distance equations and the nonlinear 3-D ranging (`Bogenschnitt') equations in a single step once the equations have been converted into algebraic (polynomial) form. In particular, the algebraic tool of the Groebner basis provides symbolic solutions to the problem of 3-D resection. The various forward and backward substitution steps inherent in the classical closed-form solutions of the problem are avoided. Similar to the Gauss elimination technique in linear systems of equations, the Groebner basis eliminates several variables in a multivariate system of nonlinear equations in such a manner that the end product normally consists of a univariate polynomial whose roots can be determined by existing programs e.g. by using the roots command in Matlab.Acknowledgments.The first author wishes to acknowledge the support of JSPS (Japan Society of Promotion of Science) for the financial support that enabled the completion of the write-up of the paper at Kyoto University, Japan. The author is further grateful for the warm welcome and the good working atmosphere provided by his hosts Professors S. Takemoto and Y. Fukuda of the Department of Geophysics, Graduate School of Science, Kyoto University, Japan.     

Wavelet evaluation of the Stokes and Vening Meinesz integrals

The wavelet transform is a powerful tool in evaluating some singular geodetic integrals. Due to its localization properties in both the time (space) and frequency (scale) domains, and because the kernels of some geodetic integrals have singular points and decay smoothly and quickly away from the singularities, many wavelet transform coefficients of the kernels become zeros or negligible, and only a small number of wavelet transform coefficients are significant. It is thus possible to significantly compress the kernels of these integrals on a wavelet basis by neglecting the zero coefficients and the small coefficients below a certain threshold. Therefore, wavelets provide a convenient way of efficiently evaluating these integrals in terms of fast computation and savings of computer memory. A modified algorithm for the wavelet evaluation of Stokes' integral is presented. The same modified algorithm is applied to the evaluation of the Vening Meinesz integral, whose kernel has a stronger singularity than does Stokes' kernel. Numerical examples illustrate the efficiency and accuracy of the wavelet methods.Acknowledgments.The author express their sincere thanks to Dr. Salamonwicz for providing his PhD thesis. E-mail correspondence between the authors and Dr. Barthelmes and Dr. Benciolini contributed to the work. R. Benciolini and the other two anonymous reviewers are thanked for their constructive comments. Support for this research was provided by research grants to Dr. Sideris from the Natural Sciences and Engineering Reserch Council of Canada (NSERC) and the Geomatics for Informed Decisions (GEOIDE) Network of Centres of Excellence. The MATLAB Wavelet Toolbox package was used as the platform to develop the software in this project.     

Biases and accuracy of, and an alternative to, discrete nonlinear filters

The biases and accuracy of the extended Kalman filter (EKF) and a second-order nonlinear filter (SONF) are discussed from the point of view of a frequentist; these are often derived by applying the relevant conditional quantities to the linear Kalman algorithm under the Bayesian framework. The EKF and the SONF are biased, although the SONF has been derived in the hope of improving first-order filters. Unfortunately the biases of the SONF may be magnified further, because the second-order terms of the relevant Bayesian conditional quantities have never been properly used to derive the SONF from the frequentist point of view. The variance–covariance matrix of the SONF given in the literature is proven to be incorrect up to the second-order approximation, and the correct one is derived. Finally, also from the point of view of a frequentist, an alternative, almost unbiased SONF is proposed, if the randomness of partials is neglected. Received: 12 July 1997 / Accepted: 5 October 1998     

Theory of integer equivariant estimation with application to GNSS

Carrier phase ambiguity resolution is the key to high-precision global navigation satellite system (GNSS) positioning and navigation. It applies to a great variety of current and future models of GPS, modernized GPS and Galileo. The so-called fixed baseline estimator is known to be superior to its float counterpart in the sense that its probability of being close to the unknown but true baseline is larger than that of the float baseline, provided that the ambiguity success rate is sufficiently close to its maximum value of one. Although this is a strong result, the necessary condition on the success rate does not make it hold for all measurement scenarios. It is discussed whether or not it is possible to take advantage of the integer nature of the ambiguities so as to come up with a baseline estimator that is always superior to both its float and its fixed counterparts. It is shown that this is indeed possible, be it that the result comes at the price of having to use a weaker performance criterion. The main result of this work is a Gauss–Markov-like theorem which introduces a new minimum variance unbiased estimator that is always superior to the well-known best linear unbiased (BLU) estimator of the Gauss–Markov theorem. This result is made possible by introducing a new class of estimators. This class of integer equivariant estimators obeys the integer remove–restore principle and is shown to be larger than the class of integer estimators as well as larger than the class of linear unbiased estimators. The minimum variance unbiased estimator within this larger class is referred to as the best integer equivariant (BIE) estimator. The theory presented applies to any model of observation equations having both integer and real-valued parameters, as well as for any probability density function the data might have. AcknowledgementsThis contribution was finalized during the authors stay, as a Tan Chin Tuan Professor, at the Nanyang Technological Universitys GPS Centre (GPSC) in Singapore. The hospitality of the GPSCs director Prof Law Choi Look and his colleagues is greatly appreciated.     

利用信息融合进行月球卫星自主导航

研究了绕月卫星自主导航方法,提出了由星敏感器、紫外月球敏感器和测高仪组成的多源信息组合导航方案。将Unscented Kalman滤波(UKF)应用于非线性导航系统,采用信息融合技术设计了相关的联邦滤波算法,实现了系统的信息互补,完成了卫星轨道的最优估计。利用数学仿真对这种导航系统的有效性进行了验证,并与基于扩展Kal man滤波(EKF)的信息融合算法进行了比较。仿真结果表明,所提出的UKF融合算法具有良好的稳定性,可进一步提高导航系统的精度。     

Modelling of irregularly sampled surfaces by two-dimensional snakes

A new method for the modelling of irregularly sampled surfaces is presented. The surfaces under consideration allow discontinuities of the first and higher derivatives. The method discussed is a generalization of the so called snakes approximation method. The variational problem, which is part of the snakes modelling, turns out to be the weighted sum of a membrane and a thin-plate-spline kernel. The variational problem is solved by the direct method using scaled and shifted Gaussians as base functions. The efficiency of the proposed method is demonstrated for a set of laser-scanning data. AcknowledgementsThe authors would like to thank Prof. Siegfried Meier from Dresden University of Technology, Germany, for his encouragement and suggestions to this work. They also wish to acknowledge the support of the KBN, State Committee for Scientific Research, Poland under grant no. 5 T12E 057 22 and the reviewers for their helpful comments.     

Nonlinear diffusion filtering of data on the Earth’s surface

The paper deals with data filtering on closed surfaces using linear and nonlinear diffusion equations. We define a surface finite-volume method to approximate numerically parabolic partial differential equations on closed surfaces, namely on a sphere, ellipsoid or the Earth’s surface. The closed surface as a computational domain is approximated by a polyhedral surface created by planar triangles and we construct a dual co-volume grid. On the co-volumes we define a weak formulation of the problem by applying Green’s theorem to the Laplace–Beltrami operator. Then the finite-volume method is applied to discretize the weak formulation. Weak forms of elliptic operators are expressed through surface gradients. In our numerical scheme we use a piece-wise linear approximation of a solution in space and the backward Euler time discretization. Furthermore, we extend a linear diffusion on surface to the regularized surface Perona–Malik model. It represents a nonlinear diffusion equation, which at the same time reduces noise and preserves main edges and other details important for a correct interpretation of the real data. We present four numerical experiments. The first one has an illustrative character showing how an additive noise is filtered out from an artificial function defined on a sphere. Other three examples deal with the real geodetic data on the Earth’s surface, namely (i) we reduce a stripping noise from the GOCE satellite only geopotential model up to degree 240, (ii) we filter noise from the real GOCE measurements (the component $T_{zz})$ , and (iii) we reduce a stripping noise from the satellite only mean dynamic topography at oceans. In all experiments we focus on a comparison of the results obtained by both the linear and nonlinear models presenting advantages of the nonlinear diffusion.     

乘性误差模型参数估计及精度评定的Sterling插值方法

乘性误差模型加权最小二乘参数估值是观测值的非线性函数,观测值的权是加权最小二乘参数估值的非线性函数。已有的乘性误差模型参数估计方法理论上可以达到二阶无偏,但精度评定方法只能达到一阶精度,并且参数估计逐步的迭代过程使得参数及改正数的每一步估值都具有随机性,使得最终的参数估值与观测值为复杂的非线性关系。考虑到非线性迭代过程对加权最小二乘参数带来的影响,使用一种无需求导的Sterling插值方法求解参数估值的均值和标准差。模拟实验表明,当模型非线性较高时,考虑每次迭代的随机性对参数估值的影响可以得到更接近真值的参数估值,并且所提方法的精度评定可以达到二阶精度,验证了Sterling插值方法用于乘性误差模型参数估计及其精度评定的适用性和有效性。     

Nonlinear least-squares method via an isomorphic geometrical setup

The resolution of a nonlinear parametric adjustment model is addressed through an isomorphic geometrical setup with tensor structure and notation, represented by a u-dimensional “model surface” embedded in a flat n-dimensional “observational space”. Then observations correspond to the observational-space coordinates of the pointQ, theu initial parameters correspond to the model-surface coordinates of the “initial” pointP, and theu adjusted parameters correspond to the model-surface coordinates of the “least-squares” point . The least-squares criterion results in a minimum-distance property implying that the vector Q must be orthogonal to the model surface. The geometrical setup leads to the solution of modified normal equations, characterized by a positive-definite matrix. The latter contains second-order and, optionally, thirdorder partial derivatives of the observables with respect to the parameters. This approach significantly shortens the convergence process as compared to the standard (linearized) method.     

联合EEMD-KECA算法的InSAR干涉相位时频滤波

根据干涉图信号和噪声时频分布差异的特点,提出一种改进的基于经验模态分解EEMD的InSAR干涉相位滤波方法。该方法首先利用可有效降低模态混叠的EEMD算法,对干涉图的实部及虚部分别进行2维经验模态分解,获得具有不同时间尺度的模态分量;然后根据信号和噪声分量的时间尺度分布特性的差异,采用适用于非线性信号分析的KECA算法对噪声识别、分离;最后利用去除噪声后的模态分量重构干涉图。为了证明本文方法的有效性,分别利用模拟数据及真实InSAR差分干涉相位进行滤波试验。对比本文EEMD-KECA滤波方法、Goldstein滤波、圆周期—中值滤波、EMD分解、EMD-PCA方法的滤波效果,采用相干斑指数、均方差指数、边缘保持指数进行定量评价。结果表明,与经典InSAR干涉图滤波方法相比,本文联合EEMD-KECA算法的滤波方法能有效滤除干涉图噪声,且在条纹边缘等细节信息的保持上也具有较大优势。     

Solution to the spectral filter problem of residual terrain modelling (RTM)

In physical geodesy, the residual terrain modelling (RTM) technique is frequently used for high-frequency gravity forward modelling. In the RTM technique, a detailed elevation model is high-pass-filtered in the topography domain, which is not equivalent to filtering in the gravity domain. This in-equivalence, denoted as spectral filter problem of the RTM technique, gives rise to two imperfections (errors). The first imperfection is unwanted low-frequency (LF) gravity signals, and the second imperfection is missing high-frequency (HF) signals in the forward-modelled RTM gravity signal. This paper presents new solutions to the RTM spectral filter problem. Our solutions are based on explicit modelling of the two imperfections via corrections. The HF correction is computed using spectral domain gravity forward modelling that delivers the HF gravity signal generated by the long-wavelength RTM reference topography. The LF correction is obtained from pre-computed global RTM gravity grids that are low-pass-filtered using surface or solid spherical harmonics. A numerical case study reveals maximum absolute signal strengths of \(\sim 44\) mGal (0.5 mGal RMS) for the HF correction and \(\sim 33\) mGal (0.6 mGal RMS) for the LF correction w.r.t. a degree-2160 reference topography within the data coverage of the SRTM topography model (\(56^{\circ }\hbox {S} \le \phi \le 60^{\circ }\hbox {N}\)). Application of the LF and HF corrections to pre-computed global gravity models (here the GGMplus gravity maps) demonstrates the efficiency of the new corrections over topographically rugged terrain. Over Switzerland, consideration of the HF and LF corrections reduced the RMS of the residuals between GGMplus and ground-truth gravity from 4.41 to 3.27 mGal, which translates into \(\sim 26\)% improvement. Over a second test area (Canada), our corrections reduced the RMS of the residuals between GGMplus and ground-truth gravity from 5.65 to 5.30 mGal (\(\sim 6\)% improvement). Particularly over Switzerland, geophysical signals (associated, e.g. with valley fillings) were found to stand out more clearly in the RTM-reduced gravity measurements when the HF and LF correction are taken into account. In summary, the new RTM filter corrections can be easily computed and applied to improve the spectral filter characteristics of the popular RTM approach. Benefits are expected, e.g. in the context of the development of future ultra-high-resolution global gravity models, smoothing of observed gravity data in mountainous terrain and geophysical interpretations of RTM-reduced gravity measurements.     

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.     

An algorithm for the inverse of a multivariate homogeneous polynomial of degreen

Summary Various geodetic problems (the free nonlinear geodetic boundary value problem, the computation of Gauß-Krüger coordinates or UTM coordinates, the problem of nonlinear regression) demand theinversion of an univariate, bivariate, trivariate, in generalmultivariate homogeneous polynomial of degree n. The new algorithm which is oriented towardsSymbolic Computer Manipulation is based upon the algebraic power base computation with respect toKronecker-Zehfu product structure leading to the solution of a system oftriangular matrix equations: Only the first row of the inverse triangular matrix has to be computed. TheSymbolic Computer Manipulation program of the GKS algorithm is available from the authors.     

Harmonic analysis of the Earth's gravitational field by means of semi-continuous ephemerides of a low Earth orbiting GPS-tracked satellite. Case study: CHAMP

An algorithm for the determination of the spherical harmonic coefficients of the terrestrial gravitational field representation from the analysis of a kinematic orbit solution of a low earth orbiting GPS-tracked satellite is presented and examined. A gain in accuracy is expected since the kinematic orbit of a LEO satellite can nowadays be determined with very high precision, in the range of a few centimeters. In particular, advantage is taken of Newton's Law of Motion, which balances the acceleration vector with respect to an inertial frame of reference (IRF) and the gradient of the gravitational potential. By means of triple differences, and in particular higher-order differences (seven-point scheme, nine-point scheme), based upon Newton's interpolation formula, the local acceleration vector is estimated from relative GPS position time series. The gradient of the gravitational potential is conventionally given in a body-fixed frame of reference (BRF) where it is nearly time independent or stationary. Accordingly, the gradient of the gravitational potential has to be transformed from spherical BRF to Cartesian IRF. Such a transformation is possible by differentiating the gravitational potential, given as a spherical harmonics series expansion, with respect to Cartesian coordinates by means of the chain rule, and expressing zero- and first-order Ferrer's associated Legendre functions in terms of Cartesian coordinates. Subsequently, the BRF Cartesian coordinates are transformed into IRF Cartesian coordinates by means of the polar motion matrix, the precession–nutation matrices and the Greenwich sidereal time angle (GAST). In such a way a spherical harmonic representation of the terrestrial gravitational field intensity with respect to an IRF is achieved. Numerical tests of a resulting Gauss–Markov model document not only the quality and the high resolution of such a space gravity spectroscopy, but also the problems resulting from noise amplification in the acceleration determination process.     

A modified Wiener-type filter for geodetic estimation problems with non-stationary noise

 One of the most basic and important tools in optimal spectral gravity field modelling is the method of Wiener filtering. Originally developed for applications in analogue signal analysis and communication engineering, Wiener filtering has become a standard linear estimation technique of modern operational geodesy, either as an independent practical tool for data de-noising in the frequency domain or as an integral component of a more general signal estimation methodology (input–output systems theory). Its theoretical framework is based on the Wiener–Kolmogorov linear prediction theory for stationary random fields in the presence of additive external noise, and thus it is closely related to the (more familiar to geodesists) method of least-squares collocation with random observation errors. The main drawback of Wiener filtering that makes its use in many geodetic applications problematic stems from the stationarity assumption for both the signal and the noise involved in the approximation problem. A modified Wiener-type linear estimation filter is introduced that can be used with noisy data obtained from an arbitrary deterministic field under the masking of non-stationary random observation errors. In addition, the sampling resolution of the input data is explicitly taken into account within the estimation algorithm, resulting in a resolution-dependent optimal noise filter. This provides a more insightful approach to spectral filtering techniques for noise reduction, since the data resolution parameter has not been directly incorporated in previous formulations of frequency-domain estimation problems for gravity field signals with discrete noisy data. Received: 1 November 2000 / Accepted: 19 June 2001     

SHORT NOTE: Extending simplified high-degree synthesis methods to second latitudinal derivatives of geopotential

 Simplified techniques for high-degree spherical harmonic synthesis are extended to include gravitational potential second derivatives with respect to latitude. Received: 23 July 2001 / Accepted: 12 April 2002 Acknowledgement. The authors would like to thank Christian Tscherning for recommending Laplace's equation as an accuracy test. Our use of Legendre's differential equation, as the most direct means for extending our simplified synthesis methods to second-order derivatives, was a direct result of this suggestion. Correspondence to: S. A. Holmes     

扩展区间Kalman滤波器及其在GPS/INS组合导航中的应用

针对具有不确定动态模型参数的 GPS/INS 组合导航系统,首先介绍一种新型的区间Kalman滤波器,讨论了GPS/INS 组合系统中模型参数不确定性的问题,分析了惯性传感器建模中相关时间常数的区间特性,并建立了适合非线性特性的GPS/INS组合系统的扩展区间卡尔曼滤波器.计算结果表明,扩展区间卡尔曼滤波器对非线性GPS/INS组合系统是很有效的,它能给出组合系统导航误差的上下界,这对组合系统的设计具有指导的意义.     

Formulas for precisely and efficiently estimating the bias and variance of the length measurements

Error analysis in length measurements is an important problem in geographic information system and cartographic operations. The distance between two random points—i.e., the length of a random line segment—may be viewed as a nonlinear mapping of the coordinates of the two points. In real-world applications, an unbiased length statistic may be expected in high-precision contexts, but the variance of the unbiased statistic is of concern in assessing the quality. This paper suggesting the use of a k-order bias correction formula and a nonlinear error propagation approach to the distance equation provides a useful way to describe the length of a line. The study shows that the bias is determined by the relative precision of the random line segment, and that the use of the higher-order bias correction is only needed for short-distance applications.     

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.