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 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.

In the atom gravimeter, three Raman pulses are utilized to realize the interference of atom matter waves, and atom interference fringes are obtained by scanning the chirp rate of the Raman laser during the interference time. Previously, fringe data analysis methods used LS (Least Squares) to fit the cosine function of each interference fringe data to minimize the standard deviation between the estimated value and the observed value of each group of fringe data or the EKF (Extended Kalman Filter) method to obtain the estimation of the gravity value. In this paper, we propose a new method applied to the interference fringe fitting of the atom gravimeter, namely, through the FPSO (Fitness Particle Swarm Optimization) method to estimate the parameters of the interference fringe atom and then estimate the gravity value. First, the theoretical analysis and proof are carried out by using simulation data. On this basis, we carried out a gravity measurement experiment in the ship-mounted mooring state, which further verified the feasibility and effectiveness of the algorithm. The simulation and experimental results show that, compared with LS and EKF methods, the FPSO method can search the relatively optimal fitting parameters of atom interference fringes quickly and accurately and improve the accuracy and stability of the atom gravimeter measurement. It is feasible and effective to apply the FPSO method to fitting atom interference fringes. The FPSO method proposed in this paper can be used as a new method for fitting atom interference fringes, which provides a new idea and choice for accurate gravity measurement in a dynamic environment.

As a high-precision gravity measuring device, a marine atomic gravimeter is highly sensitive to vibration signals. Accurate measurement and analysis of vibration signal is the primary condition to realize vibration compensation and vibration suppression. Denoising plays a crucial role in the processing of these vibration signals. The vibration signals of a marine gravimeter contain numerous nonlinear and nonstationary components. In this paper, a vibration signal denoising method of marine atomic gravimeter based on improved variational mode decomposition (VMD) was put forward to effectively suppress the noise. An improved genetic particle swarm optimization (GPSO) was first adopted for the parametric optimization of VMD by taking minimum permutation entropy (PE) as fitness function and adaptively determining the optimal parameters of VMD. PE was then utilized to calculate the proportion of noise-containing components in the intrinsic mode function (IMF) components obtained by VMD. The components were classified into noise and signal components by searching for the mutation points of two adjacent IMF permutation entropies. On this basis, noise components were denoised by Savitzky-Golay (SG) filter. In the end, the denoised components were reconstructed with the signal components to generate denoised vibration signals. To verify the effectiveness, the proposed method was applied in denoising, simulated and measured vibration signals of a marine atomic gravimeter, and compared with Daubechies (db) wavelet, Symlets (sym) wavelet, and empirical mode decomposition (EMD). The results showed that the proposed method could effectively remove the noise from nonlinear vibration signals and retain the authentic and useful information, so that it was able to provide the supporting data for gravity compensation of marine atomic gravimeter.

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