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.

提出了一种鲁棒的卡尔曼滤波算法来解决存在离群值的非线性变量动态误差问题。该算法是鲁棒约束的集成总卡尔曼滤波器（RCITKF）。当可观察量被严重误差（离群值）污染时，该方法会迭代地对预测解决方案进行加权。它可以施加可能在某些问题中出现的二次约束。此外，RCITKF算法可以考虑动态问题功能模型的被忽略的随机未知数，与已知的卡尔曼滤波器相比，它具有更多的优势。在两个大地测量应用中，与扩展卡尔曼滤波器（EKF）和无味卡尔曼滤波器（UKF）算法相比，这些算法的效率得到了证明。