There are various kinds of noises in the raw data observed by airborne gravimetry.Kalman filtering is one of the technologies for obtaining the gravity anomaly from the data.Standard Kalman filtering is capable of obtaining precision gravity anomaly based on accurate statistical noise information,but the noise information is difficult to obtain in practice.Therefore,the measurement noise adaptive Kalman filter based on the analysis of Sage-Husa adaptive Kalman filtering is designed in this paper.Then the adaptive Kalman filter combined with fixed-interval smoother is applied to process simulation data.It can be seen from the simulation result that the errors caused by filter non-convergence are eliminated by fixed-interval smoother.Moreover,compared with the gravity anomaly estimated by standard Kalman filter,accurate gravity anomaly is extracted from the noised data by the adaptive filter when measurement noise information is unknown.