On robust constrained Kalman filter for dynamic errors-in-variables model

Abstract

A robust Kalman filter algorithm is proposed to solve nonlinear errors-in-variables dynamic problems in the presence of outliers. This algorithm is robust constrained integrated total Kalman filter (RCITKF). The method iteratively reweights the predicted solution when the observable quantities are contaminated by gross errors (outliers). It can impose the quadratic constrains which may appear in some problems. Moreover, the RCITKF algorithm can consider the neglected random unknowns of the functional model of the dynamic problem which gives an added advantage over the previous Kalman filters. In two geodetic applications, the efficiency of these algorithms is demonstrated in contrast to the extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms.