Geodesy by radio interferometry: The application of Kalman Filtering to the analysis of very long baseline interferometry data

Abstract

We discuss the application of Kalman filtering techniques to the analysis of very long baseline interferometry (VLBI) data. The VLBI observables are geometrically related to the geodetic and astrometric parameters which can be determined from them. However, contributions to the observables from the clocks at, and the atmospheres above, the VLBI sites must be accounted for if reliable estimates of geodetic and astrometric parameters are to be obtained. Here an implementation of a Kaiman filter to account for stochastic behavior on those parameters which vary during the course of a VLBI experiment is discussed. Both the nature of the stochastic processes which should be used in the model for the VLBI data and the implementation of the Kaiman filter estimator are considered. From the results obtained, we conclude that the Kaiman filter is appropriate for analyzing VLBI data. The choice of stochastic model does not unduly affect the estimates of the geodetic parameters and the quality of these estimates is higher than that for conventional weighted least squares estimators.