An atomic interference gravimeter (AIG) is of great value in underwater aided navigation, but one of the constraints on its accuracy is vibration noise. For this reason, technology must be developed for its vibration isolation. Up to now, three methods have mainly been employed to suppress the vibration noise of an AIG, including passive vibration isolation, active vibration isolation and vibration compensation. This paper presents a study on how vibration noise affects the measurement of an AIG, a review of the research findings regarding the reduction of its vibration, and the prospective development of vibration isolation technology for an AIG. Along with the development of small and movable AIGs, vibration isolation technology will be better adapted to the challenging environment and be strongly resistant to disturbance in the future.

The atom gravimeter is a novel type of gravimeter that offers notable advantages, including high sensitivity and long-term stability. However, the accuracy and reliability of measurements obtained from atom gravity measurement systems are significantly influenced by vibrations. To address this issue, this paper presents the design and development of an active and passive vibration isolation system aimed at reducing vibrations affecting the Raman mirror during dynamic measurements in atom gravimeters. The system is based on the passive vibration isolation platform. To meet the vertical vibration isolation requirements of the atom gravimeter and to be applicable in dynamic measurement applications, linear bearings are utilized to restrict the horizontal movement of the vibration isolation platform while allowing linear movement in the z-axis direction. This effectively enhances the vibration isolation performance in the vertical direction. Additionally, three voice coil motors are evenly distributed on the vibration isolation platform at 120° intervals to prevent system instability and inclination caused by uneven force. Furthermore, a control strategy that combines adaptive feedforward control and PID feedback control is implemented to further improve the vibration isolation performance of the system. Experimental results demonstrate that the maximum vibration in the vertical direction can be suppressed by 98.34% in the frequency band below 10Hz, which is particularly sensitive to the atom interferometer. The impact of vibration on the sensitivity of the cold atom gravimeter is two orders of magnitude lower than that experienced on the ground, significantly enhancing the accuracy of the atom gravimeter. These findings have substantial practical value and can be applied in scientific research and industrial applications within the field of atomic gravity measurement. They provide positive implications for the development of advanced dynamic measurement techniques for atom gravimeters.

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 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 survey line is proposed, which can evaluate the matching capability online according to the position of the underwater vehicle and adaptively adjusts 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.

The navigation performance of an autonomous underwater vehicle (AUV) as the main tool for exploring the ocean greatly affects its work efficiency. Under the circumstance that high-precision GNSS positioning signals cannot be obtained, the role of the Strapdown Inertial Navigation System/Doppler Velocity Log (SINS/DVL) integrated navigation system is becoming more prominent. Due to marine creatures or the seafloor topography, DVL is prone to outliers or even failures during measurement. To solve these problems, a LSTM/SVR-VBAKF algorithm aided integrated navigation system is proposed. First, under normal circumstances of DVL, the output information of SINS and DVL are used as training samples, and they train the Long Short-Term Memory (LSTM) model. To enhance the robustness and adaptability of the filter, a novel variational Bayesian adaptive filtering algorithm based on support vector regression is proposed. When the DVL formation is missing, the deep learning method adopted in this paper will be continuously output to ensure the effect of integrated navigation. The shipboard test data is verified from two aspects: filter performance and neural network-assisted integrated navigation system capability. The experimental results show that the new method proposed in this paper can effectively handle a situation where DVL output is not available.

With the improvement of resolution and precision of marine gravity anomaly data from satellite altimetry, the gravity matching navigation of underwater vehicles has been supported by background field data. To improve the accuracy, a multi-index model for analyzing the gravity background field characteristics is constructed, and an adaptive matching navigation algorithm is designed. First, the common single-index feature analysis methods are introduced, and their advantages and disadvantages are analyzed. The multi-index average statistical parameter (MASP) model applicable to the characterization of marine gravity anomaly background field is designed, and the reliability is verified by two satellite altimetric gravity anomaly datasets from the northwest Indian Ocean and the western Pacific Ocean. Then, an adaptive matching navigation algorithm is proposed by introducing the MASP model in the filtering process, which can adjust the filter structure in real time. Finally, the performance of the method is verified by about 30 h of field test. The matching results show that the proposed method can adjust the filter structure adaptively and has better comprehensive performance.

The atomic interference gravimeter, characterized by its high sensitivity and precision in gravity measurement, is significantly influenced by vibration noise in field environments. The vibration correction is a feasible approach to mitigate the constraints imposed by vibration noise in measurements, thereby enhancing accuracy. The vibration noise of the shipborne environment is intensified, leading to the complete disruption of the traditional chirp fringes. We analyzed the correlation between the vibration phase and atom population and developed an acceleration phase fringe that is suitable for the shipborne environment. Subsequently, to enhance the precision of calibrating the transfer function resulting from the inconsistency between the centers of the accelerometer mass and the Raman reflecting mirror, we proposed a second-order coupling acceleration transfer model as a substitute for the tradition model. An intelligent optimization algorithm was employed to optimize the model parameters. Finally, an atomic gravimeter system was constructed to assess the proposed model and method in the context of shipborne. The results indicate that the proposed second-order coupling model demonstrates superior calibration efficacy compared to the traditional model. Compared with the uncalibrated, the standard deviation of drift phase residuals shows an average decrease of 87.8%, and the root mean square error of fitting decreases by 53.7%. The improvement in both short-term accuracy and long-term stability of mooring gravity measurement is substantial. The ocean navigation gravimetry shows better repeatability and external consistency. Compared with the uncalibrated, the internal and external coincidence accuracy improved by 93.6% and 92.0%, respectively.