Aiming at the problem of inaccurate state estimation caused by measurement outliers during the use of the global navigation satellite system, a RKFOF(robust Kalman filtering with outlier suppression) technology was studied. Firstly, it was assumed that all matrices of the system and measurement models were affected by norm-bounded parameter uncertainties. A purely deterministic perspective was adopted, and a penalty function was applied to transform it into a robust regularized least squares estimation problem. Then, the characteristic of the normal distribution of the innovation vector was used to determine the dynamic threshold of outliers. Finally, the optimal Kalman gain was obtained by differentiating the covariance matrix. By assimilating offset outliers into the measurement model, the influence of outliers was mitigated through the statistics of the outlier offset. Compared with algorithms such as RKF(robust Kalman filter), WKF(weighted Kalman filter), and ORF(outlier rejection filter), experiments show that when the outlier rate is less than 30%, the effects of these algorithms are similar. When the number of outliers is greater than 30%, the root mean square error of the RKFOF algorithm is reduced by more than 42.3%, 16.3%, and 13.1% respectively compared with the above algorithms.