A Modified Discrete Karhunen-Loeve Random Field Estimator
Based upon the modified Karhunen-Loeve model of the gravity disturbance vector proposed by us recently, a corresponding Karhunen-Loeve random field estimator is developed in this paper for a two-dimensional grid of gravity data in a local finite region.The new eigenvectors obey the "required" orthogonality relations on the chosen grid provided the KL expansion is now separated into odd and even integers. The closed-form solution of the estimator isthen obtained under the "diagonal" assumption for the gain coefficients. It is shown that this assumption, without which nosolution is possible, enables the estimator to reproduce the data atgrid points when the noise in the data is absent and the number of terms in the Karhunen-Loeve expansion are equal to the number of grid points.
Two-dimensional smoothing of a vector Laplacian random field with application to geodesy (Ph.D. Thesis abstr.)
A two-dimensional signal processing algorithm is developed to obtain smoothed estimates of a vector random field when observed in the presence of additive two-dimensional white noise. The vector random field consists of a two-dimensional scalar random field and its two orthogonal derivatives, each of which can be scaled arbitrarily by a constant. The signal model consists of the Laplacian operator acting on the two-dimensional scalar random field excited by two-dimensional white noise. Such a signal model that permits a nonstationary and nonisotropic random field is "spatially dynamic" as opposed to a static model specified by autocorrelation functions or power spectral densities. The model is also "bilateral" such that any given spatial point’s dependency upon all its neighboring points is preserved and no unnecessary limitation of causality is enforced on the two-dimensional system. To preserve the noncausality of the problem, the estimation algorithm must not order the two-dimensional data since there is no sense in which any data point can be said to be "prior" to any other data point. To this end, a two-dimensional smoothing algorithm is developed first in the continuous domain by making use of a two-dimensional Karhunen-Loeve expansion that makes no specific reference to either the geometry or the ordering of the parameter space.
Regional adjustment of inertial gravity disturbance vector measurements by optimal two-dimensional smoothing
A two-dimensional signal processing algorithm is developed to obtain smoothed estimates of the gravity disturbance vector from vector measurements obtained by an inertial surveying system. The method differs from a conventional least squares regional adjustment of such measurements in that it accommodates a signal model in the smoothing process. Using principles from the physical theory of geodesy, it is shown that for a local region on the surface of the earth, an appropriate signal model is obtained by applying the two-dimensional Laplacian operator to a function representing the surface disturbance potential and equating the result to spatial white noise. The model of the vector measurement is the three orthogonal spatial derivatives of a three dimensional disturbance potential evaluated at the surface contaminated by additive white noise. The problem of simultaneous smoothing of all the gravity disturbance measurements from all survey traverses in the region is solved by representing the surface disturbance potential by a two-dimensional Karhunen-Loeve expansion that makes no specific reference to either the geometry or the ordering of the parameter space, thereby making no assumptions of causality, stationarity or isotropy. The problem of estimating the gravity anomaly and the two vertical deflection components reduces to estimating the Karhunen-Loeve coefficients which are uncorrelated and rapidly converging. Simulation results as well as smoothing of actual gravity disturbance vector measurements obtained by the U.S. Army Engineer Topographic Laboratories (USAETL) with the Rapid Geodetic Survey System (RGSS) at the White Sands Missile Range (WSMR) are presented in the paper. An analysis of these results shows that the optimal two-dimensional smoother obtains a performance benefit relative to conventional regional least squares by a factor of 2 and a benefit relative to single-traverse smoothed results by a factor of 4.SommaireUn algorithme de traitement du signal en deux dimensions est développé pour obtenir une estimation lissée du vecteur de la perturbation de la pesanteur à partir des mesures de vecteur obtenues avec un système d’arpentage inertiel. La méthode diffère d’un compensation régionale conventionnelle par moindres carrés de telles mesures, par le fait qu’elle contient un modèle du signal dans le processus de compensation. En s’appuyant sur les principes de la géodésie physique, il est montré que pour une région locale de la surface de la terre, un modèle approprié du signal est obtenu en appliquant l’opérateur à deux dimensions de Laplace à une fonction représentant le champ perturbateur à la surface de la terre et égalisant le résultat à un bruit blanc spatial. Le modèle du vecteur de mesures est défini par les trois dérivées spatiales de la fonction tridimensionnelle du potentiel perturbateur évaluées à la surface et contaminées par un bruit blanc. Le problème du lissage de toutes les mesures de gravité perturbatrice obtenues à partir des polygonales effectuées est résolu en représentant le potentiel perturbateur à la surface à l’aide d’un développement Karhunen-Loeve à deux dimensions qui ne fait aucunement référence à la géométrie ou à l’ordre des paramètres; ceci prévient toute dépendance spatiale des points adjacents. Le problème de l’estimation de l’anomalie de la gravité et des deux composantes de la déviation de la verticale se réduit à celle des coefficients Karhunen-Loeve qui sont non-corrélés et convergent rapidement. Les résultats de simulation aussi bien que le lissage des données du vecteur de perturbation de la pesanteur foumi par l’U.S. Army Engineer Topographics Labs (USAETL) sont présentés. L’analyse de ces résultats montre que le lissage optimal à deux dimensions améliore les résultats par un facteur 2 comparés aux résultats d’une compensation régionale par moindres carrés, et par un facteur 4 comparés aux résultats lissés d’une simple traverse.