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.

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.

According to the problems that the velocity of OD(Odometer, OD) and DVL (Doppler Velocity Log, DVL) is easily contaminated by non-Gaussian noise, and the priori information of state vector is usually hard to estimate accuracy enough, which will lead performance of alignment to decline or even divergent. Considering the error characteristic model has very significant nonlinear characteristics. A RUIKF (Robust Unscented Information Kalman Filter, RUIKF) algorithm for dynamic initial alignment is proposed in this paper to suppress the influence of interference noise. The RUIKF algorithm firstly calculate the MD(Mahalanobis distance, MD) between observation and the innovation of observation, and an amplification factor is designed to modify the estimation of the measurement noise covariance matrix, and then take the modified the measurement noise covariance matrix to update measurement. The state error is estimated by RUIKF to modify the SINS (Strapdown inertial navigation system, SINS) navigation solution. Initial alignment experiments are carried out by RUIKF and UIKF (unscented information Kalman filter,UIKF) algorithm respectively under the conditions of outliers and mixed Gaussian distribution noise. The results show that RUIKF can suppress the effect of outliers and non-Gaussian noise when the velocity of OD(or DVL) is contaminated. The experiment results also verify the feasibility and effectiveness of the RUIKF algorithm on SINS/DVL/OD integrated dynamic initial alignment.

Due to the complex underwater environment, the assisted velocity provided by Doppler velocity log (DVL) may be contaminated by non-Gaussian noise. This will degrade the performance of the traditional optimisation-based alignment (OBA) method. To solve this problem, this paper proposes a robust OBA (ROBA) based on projection statistics (PS) algorithm. The ROBA contains two main steps which are also the contributions of the work presented here. Firstly, ROBA makes use of PS algorithm to identify the stored abnormal auxiliary velocity accurately under the framework of Kalman filter (KF), and then a robust strategy of the observation vector is designed to achieve the robustness of OBA. The semi physical experiment of SINS underwater in-motion coarse alignment is carried out based on the ship-mounted measured data. The experimental results demonstrate the superiority of the proposed method over the traditional ones, and the yaw alignment accuracy of ROBA is at least 30% improvement over the traditional ones under the non-Gaussian conditions.

The problem of dynamic attitude alignment for strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation has become a research hotspot. Underwater complex environment makes DVL output vulnerable to non‐Gaussian noise pollution, which makes it difficult to converge the SINS misalignment angle to within 1° based on analytical coarse alignment. This is to say, the performance of SINS fine alignment via Kalman filter will be degraded because the model of initial alignment exhibits non‐linearity. The inaccurate and/or unknown prior information of measurement noise statistical characteristics will also degrade the filtering performance. To ensure the SINS/DVL alignment accuracy under non‐linear, non‐Gaussian and uncertain conditions, this paper proposed a robust adaptive unscented Kalman filter (UKF) based on Variational Bayesian (VB) method (VBRAUKF). The proposed VBRAUKF improves the adaptability and robustness of UKF from the following two main aspects. Firstly, an adaptive estimation strategy for the measurement noise covariance is designed based on the VB algorithm, which can restrain the effect of inaccurate observation model and improve the adaptive ability of the filtering method. Secondly, an expansion factor is designed based on Mahalanobis distance algorithm, which can restrain the effect of non‐Gaussian noise and improve the robustness of the filtering method. The experimental results for the problem of the SINS/DVL dynamic alignment under mixed Gaussian noise and/or outlier conditions demonstrate the superiority of the proposed VBRAUKF over the traditional ones.

To address the problem of the bottom velocity being directly affected by the time-varying ocean currents when DVL operates in the water observation mode, it cannot be directly used for combined SINS/DVL navigation. Existing methods generally approximate small-scale, short-term currents as constant; however, this assumption is inconsistent with reality over longer durations. When the conventional Kalman filter (KF) algorithm incorporates currents into the state vector, their velocities become entangled with the SINS errors, limiting estimation accuracy. This paper proposes an augmented observation algorithm (AOA) that achieves error decoupling by enhancing DVL observation and deriving the observable current velocity equation without needing external observation information. This approach effectively estimates time-varying currents. The results from simulations and shipboard tests show that, compared to the reference algorithm (Augmented Observation Quantity Filtering algorithm (AOQ)), the proposed AOA significantly decreases the root mean square error (RMSE) of time-varying current velocity estimation by more than 67%. Additionally, the RMSE of the positioning accuracy of the combined SINS/DVL navigation is improved by over 68%.