The Applications Of Robust Estimation Combining Least Squares Collocation In Repeated Gravity Date.

This article separately described the basic principles of least squares collocation ,and particularly discussed how to make certain the experiential covariance function , as well as the applications of robust estimation combining least squares collocation in repeated gravity date . Studying how to apply least squares collocation, this paper compared with least squares collocation method and several interpolation methods in physical geodesy in some areas, and preliminary discussed the practicability using the least squares in the field. Although least squares can obtain excellent evaluation, there are some specific issues, such as coarse errors among observations, that is to say, least squares hasn't the ability of anti-jamming. In order to solve this disfigurement, the author introduced robust estimation, illustrated the use of robust estimation in physical geodesy and solve the insufficiency which the least squares estimation. In this paper, penman thought over some commonly interpolation methods in physical geodesy, and pointed out some questions which should be noticed during the course of data processing, as well as put forward the good and bad points of these interpolation methods; This article systemic expatiated the theory of least squares, and debated the doubtless means of spatial covariance function and the experiential covariance function, and the method of least squares collocation's mathematic essence is the least squares adjustment which belongs to the Hilbert space with nucleus ; what's more, it particular showed equivalent weight principle and several commonly estimated projects, and explained good and bad points of all kinds of Robust Estimations, and how to apply them correctly ; Discussed how to process the observation value when the observations have coarse error, as well as how to make sure covariance function when use the least squares. First of all, this article adhibited the known signal to robust estimation and impose covariance function to robust fitting, then carried through the least squares collocation processing; we drew the conclusion that we can make use of the observation value differance of repeated gravity anomaly to be allowed to use the known repetition gravity anomaly observation value to interpolated computation to obtain the no observation point gravity anomaly value which satisfies the precision request, thus analyzed the gravity anomaly change tendency in this area.