Robust Prediction Algorithm Based on a General EIV Model for Multiframe Transformation

Abstract

In modern geodesy, there are cases in which the target frame is unique and there is more than one source frame. Helmert transformations, which are extensively used to solve transformation parameters, can be separately solved between the target frame and one of the source frames. However, this is not globally optimal, even though each transformation is locally optimal on its own. Additionally, this also generates the problem of multiple solutions in the noncommon station of the target frame. Moreover, least squares solutions can cause estimation value distortion, with a gross error existing in observations. Thus, in this paper, Helmert transformations among three frames, that is, one target frame and two source frames, are studied as an example. A robust prediction algorithm based on the general errors-in-variables prediction algorithm and the robust estimation is derived in detail and is applied to achieve multiframe total transformation. Furthermore, simulation experiments were conducted and the results validated the superiority of the proposed total transformation method over classical separate approaches.