The Research Of Full-tensor Multi-feature Fusion Gravity Gradient Matching Area Selection Criteria

The inertial navigation system used as a kind of passive and autonomous navigation equipment is a necessary system for underwater vehicle.However,due to the drift of gyroscope of the inertial navigation system,the system error will accumulate with time.In order to ensure the submarine concealment,it is necessary to use other passive means of navigation to do aided navigation of the inertial navigation system.The aided navigation of making use of the earth's own physical field is a passive navigation method.Geomagnetic aided navigation and gravity aided navigation are two important research directions.In order to realize the geomagnetic aided navigation,using measurement data of magnetic field sensor to compensate interfering magnetic field of underwater vehicle,otherwise the accuracy can not be guaranteed.Gravity measurement could be affected by external factors such as the movement of the carrier,but the measurement of the gravity gradient is not restricted by the underwater vehicle maneuver performance.Gravity gradient aided navigation is more and more attention as a kind of passive autonomous navigation.Reference map is one of the three elements of gravity gradient aided navigation technology.And the ocean environment of the earth is complex,it is necessary to make full use of the gravity gradient full-tensor multi-feature information fusion to obtain the matching area selection criteria,so as to obtain the suitable matching area.This method can effectively improve the positioning accuracy of the aided navigation system.In this paper,we study full-tensor multi-feature fusion gravity gradient matching area selection criteria.Firstly,the concept of gravity and gravity gradient,gravity gradient forward modeling method and full tensor gravity gradient multi-feature parameters are introduced.Extracting and analyzing the multi-feature parameters of the full tensor reference map.Then,the multi-feature parameters of the 5 independent component information of the gravity gradient are extracted and counted.The influence of feature parameters on the matching performance is analyzed,and full-tensor multi-feature fusion gravity gradient matching area selection criteria is proposed.The simulation results show that the navigation matching rate can reach more than 90% in the matching area by using the criteria.Finally,based on the support vector machine method,using k-fold cross validation method and the mesh optimization method to obtain the optimal parameters of support vector machine(SVM)model.a full-tensor multi-feature fusion gravity gradient matching area selection criteria is studied.The simulation results show that the navigation matching rate can reach 95%,and the accuracy of the navigation can be improved by using the matching area selection criteria based on the support vector machine.