Kalman Filter

A Kalman filter is a recursive mathematical algorithm used to estimate the state of a dynamic system from noisy and incomplete observations. It combines prior information (prediction) with new measurements (update) in an optimal way, based on statistical assumptions about measurement and process uncertainties.

In geodesy, Kalman filters are widely applied in the analysis of time-dependent observations from GNSS, VLBI, SLR, DORIS, and satellite gravimetry. They are used for precise orbit determination, real-time positioning, estimation of Earth orientation parameters, tropospheric delays, and gravity field variations. The Kalman filter framework enables continuous, consistent, and statistically rigorous estimation of geodetic parameters.