Submarines in the underwater sailing need a safe, reliable, high accurate, and covert well navigation system. Inertial navigation system (INS) is the core of underwater navigation. But the inertial navigation system gathers information based gyroscope, accelerometer and other sensors. In accordance to Newton's laws of mechanics, their own speed, location and other information is calculated by integral recursion. Since the recursive work of INS, positioning error gradually increases with time elapsing. Gravity and gravity gradient aided navigation as a passive autonomous navigation are more and more focused on, Selection of the gravity gradient matching area is one of the key to gravity gradient matching navigation. Earth's marine area is enormous, underwater environment is complex. Take advantage of multi-feature information fusion of gravity gradient full tensor, one hand a wider range of matching area can be got, to gain wider path planning area. on the other hand, the positioning accuracy of assisted navigation system can be inproved.
The matching area selection is the foundation of gravity gradient aided navigation. In this paper, a gravity gradient matching area selection criterion is proposed, based on the principal component analysis (PCA) and analytic hierarchy process (AHP). Firstly, the features of gravity gradient are extracted and nine gravity gradient characteristic parameters are obtained. Secondly, combining PCA with AHP, a PA model is built and the nine characteristic parameters are fused based on it. At last, the gravity gradient matching area selection criterion is given. By using this criterion, gravity gradient area can be divided into matching area and non-matching area. The simulation results show that gravity gradient position effect in the selected matching area is superior to the matching area, and the matching rate is greater than 90%, the position error is less than a gravity gradient grid.
Gravity gradient is a tensor with five mutual independent components. Five gravity gradient components are complementary. Combining the gravity gradient full tensor, more detail information is contributed to gravity gradient matching aided position. Gravity gradient full tensor fusion matching aided position method is proposed in this paper. The matching strategy is particle filtering (PF) and fusion strategy is weighted fusion on the confidence coefficient of each gravity gradient component. Simulations have been done and results showed that full tensor fusion matching aided position method is more effective than the aided position method based on single gravity gradient component.
Based on Particle Filter, Gravity Gradient-Terrain aided position technology is proposed in this paper. With the
sensitivity of gravity gradient to terrain, the gravity gradient reference map can be computed from the local terrain
elevation data. The position can be actualized through matching the real-time measured gravity gradient data to the
prepared gravity gradient reference map. The most widely used approximate filtering method is the extended Kaman
filter (EKF). EKF is computationally simple but, the convergence of the state estimation for the position is not
guaranteed. Particle filter (PF) makes use of the non-linear state and measurement functions, no linearization technology
is needed. PF can assure the convergence of the state estimation which follows from the classical results on convergence
of Bayesian estimators. Simulations have been done and Particle filter has been shown to be a superior alternative to the
EKF in the gravity gradient-terrain matching navigation systems.
Based on the geophysics technology, a gravity gradient-terrain matching submarine navigation approach is proposed in this paper. The submarine's current position obtained by matching the measured gravity gradient data to the prepared gravity gradient reference map is used to correct the inertial navigation system's accumulated error. Although the precision gradiometer is in use, there is no world-wide gravity gradient map. The ocean's gravity gradient data is even scarce. Therefore, a gravity gradient matching navigation system directly utilizing the gravity gradient reference map can't be realized. With the sensitivity of gravity gradient to terrain, the gravity gradient reference map can be computed from the local terrain elevation data and the preparing approach of the gravity gradient map is proposed in detail in the paper. Since the seabed terrain elevation map, especially highly accurate offing terrain elevation map has been pre-surveyed, the location can be actualized through matching the real-time measured gravity gradient data to the prepared gravity gradient reference map. Simulations show that the submarine navigation approach on gravity gradient-terrain matching is feasible and can be put into practice.