A New Improved Adaptive Algorithm in Gravity Anomaly Processing

Abstract

In order to effectively eliminate the measurement and system noise and improve the accuracy of the gravity anomaly, based on the sage-husa filter, a modified adaptive Kalman filter is proposed. The sum of the weighted innovation sequence is used as the innovation at current time, and then system parameters Q and R can be estimated by the innovation. The adaptive algorithm is conducted theoretically and based on the real gravity data, the de-noising experiment has been emulated. The simulations indicate that both filters can effectively inhibit the noise of inertial/gravity system, but the proposed filter has a better performance than sage-husa adaptive filter.