Underwater mobile gravity measurement data processing using continuous-discrete Kalman filter

To solve the data fusion problem in underwater mobile gravity measurement, a direct estimation method based on pose information is proposed. First, the state model is established using the continuous evolution equation of the state. Then, to obtain the space model of the state for data fusion of the measured underwater mobile gravity, the measurement model is constructed according to the external measurement equation. To solve the continuous-discrete space model of the state, the numerical integration method for continuous time updates and the virtual sampling iterative method for discrete observation updates are proposed, which can be used for the time update case and the observation update case, respectively. In this way, the continuous-discrete iterative extended Kalman filter for direct pose estimation is formed. The proposed methods are compared and analyzed based on the motion simulation data of different carrier and inertial measurement unit simulation data for different accuracy levels. The method proposed in this paper has been validated by carrying out the experiments on a lake. The results show that the method proposed in this paper can minimize the effect of accuracy of initial alignment on strapdown gravimetric measurement and shows a better dynamic estimation performance.