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.

The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non‐Gaussian noise on the in‐motion alignment performance of the Doppler velocity log (DVL) velocity‐aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage–Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold—the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non‐Gaussian noise. A semi‐physical simulation experiment for SINS/DVL in‐motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non‐Gaussian noise pollution, including outliers and heavy‐tailed noise, the SHRAKF performs better than traditional methods.

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 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.