A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization.

Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields.

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 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.

Strapdown Inertial Navigation System (SINS) is the core navigation sensor of the Autonomous Underwater Vehicle (AUV), and its underwater dynamic initial alignment is a hot and difficult problem in current research. Doppler Velocity Log (DVL), as a commonly used underwater velocity sensor, can provide external observation information for SINS dynamic initial alignment. In complex underwater environment, DVL measurement is susceptible to non-Gaussian noise pollution such as outlier. As a result, the prior information of the observation could be inaccurate or unknown. Then, the initial alignment performance based on Kalman filter could be degraded or even diverged. To solve this problem, a variational Bayesian based robust adaptive information filter (VBRAIKF) alignment method is proposed in this paper. Firstly, the adaptive updating strategy of measurement noise covariance matrix is designed based on the variational Bayesian method, and the variational Bayesian based adaptive information filter (VBAIKF) method is proposed. Secondly, Mahalanobis distance algorithm is used to enhance the robustness of the alignment process. Thus, the VBRAIKF is proposed. Finally, the initial alignment performance of the navigation sensor SINS is significantly improved. Based on the on-board test data, initial alignment tests of SINS were carried out using the traditional information Kalman filter (IKF), VBAIKF and VBRAIKF respectively, under the conditions of mixed Gaussian noise and outlier pollution. The results show that, compared with IKF and VBAIKF, the proposed VBRAIKF method can estimate the measurement noise covariance matrix adaptively under the condition of inaccurate observation prior information and different types of non-Gaussian noise pollution. Moreover, it has stronger alignment robustness and higher alignment accuracy.

The navigation and positioning of underwater vehicles is challenging work, especially when there is already a large initial position error, and there is an increasing concern about how to quickly correct the position error. To enhance positioning accuracy under large initial positioning errors, this paper proposes a multi-resolution sequence method with a strong constraint marine gravity-matching navigation algorithm and validates it through field experiments. First, the fundamental principles and common optimization approaches of the standard Terrain Contour Matching (TERCOM) and Iterative Closest Contour Point (ICCP) algorithms are introduced, the limitations of the sequence-matching algorithms are analyzed, and a multi-resolution sequence-matching fusion framework is designed. Then, the constrained models incorporating positional, navigational, and gravitational parameters are designed, and a strongly constrained multi-resolution sequence-matching algorithm is proposed. Finally, the performance of the method is verified by three field test trajectories with an initial position error of about 6 nautical miles. The field test results show that the proposed method has better accuracy and reliability under large initial position errors.