Abstract: An adaptive filtering based on moving window covariance estimation is introduced after the shortcomings of covariance matrices formed by windowing residual vectors,innovation vectors and correction vectors of the dynamic states are analyzed.A new adaptive Kalman filter is developed by combining the moving window covariance and the variance component estimation.It shows that the new adaptive filtering is not only simple in calculation but also robust in controlling the measurement outliers and kinematic state disturbance.