Marine Gravity Anomaly From Satellite Altimetry: Interpolation and Matching Navigation

The background field of marine gravity anomaly from satellite altimetry is an important data support for gravity-aided navigation of underwater vehicles. To achieve long-endurance and high-precision navigation and positioning, an interpolation algorithm based on ensemble learning is proposed, and an adaptive matching navigation method is designed. First, the common interpolation theories of the marine gravity anomaly background field are introduced, and the advantages and disadvantages of these theories are analyzed. The BP-Bagging ensemble learning algorithm for marine gravity anomaly background field interpolation is designed, and the reliability is verified by two satellite altimetric gravity anomaly datasets from the western Pacific Ocean. Then, a matching navigation algorithm based on the calculation method of gravity gradient deviation on the survey line is proposed, which can evaluate the matching capability online according to the position of the underwater vehicle and adaptively adjust the filter feedback coefficients. Finally, the performance of the method is verified by field tests. The matching results show that the proposed method improves navigation accuracy while enhancing the adaptive capability of the matching algorithm and providing better overall performance. Meanwhile, the gravity background field with high resolution yields better matching results.