A New Method for Determining the Gravity Disturbance Vector in Strapdown Airborne Gravimetry

Abstract

A two-step method for determining the gravity disturbance vector based on strapdown airborne gravimeter (SAG) and Global navigation satellite system (GNSS) is proposed. First, a strapdown navigation algorithm and a Kalman filter are used to estimate and compensate for the biases of the accelerometers by the maneuvering flight of SAG. Second, the gravity disturbance vector equations of SAG are established, and the forward and smoothing Kalman filter tuned by the formula of the gravity parameter in the Markov stochastic process are used to estimate the gravity disturbance vector, the model of which is described by the second-order Markov stochastic process to separated it from the biases of the accelerometers. The validity of the above method is verified by processing the data of the repeated line flight test of SAG in the South China Sea, and the results reveal that the internal coincidence accuracy of gravity anomaly is better than 0.4 mGal, and that of the horizontal gravity disturbance is better than 2 mGal.