Adaptive Robust Filtering for Kinematic GPS Positioning

After a brief review of Sage adaptive filtering,the relations of analytical expressions and covariance matrices between the basic random vector,such as residual vector,innovation vector and correction vector of predicted state,are derived and discussed.The shortcomings of covariance matrices by windowing the residual vectors,innovation vectors and correction vectors of kinematic state,are analyzed.The recently developed robust filtering, Sage adaptive filtering and the adaptively robust filtering are compared.It is shown,by derivations and calculations,that the new adaptive filtering is not only simple in calculation but also robust in controlling the measurement outliers and kinematic state disturbing.