In view of the low accuracy of the motion parameters generated by the typical ship trajectory generator, and the fact that the problem of wind, current and wave interference is not considered, this paper establishes a new ship trajectory generator by analyzing the changes in the ship’s attitude and speed under different motion states. Through simulation, the accuracy of the main motion parameters is significantly improved compared with the typical trajectory generator; the time-varying non-uniform wind, current and wave fields are constructed, and the interference effect of wind, current and waves on ship motion is analyzed by combining the empirical formulas of force and moment; an adaptive neuro fuzzy inference system (ANFIS) based on wind, current and wave interference is designed, and the fuzzy rules of the fuzzy system are determined by training and testing the measured data; the motion parameters of superimposed wind, current and wave interference are compared with the measured data, and the accuracy is further improved after superimposing wind, current and wave interference.

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′.

The initial attitude error is challenging to satisfy the requirements of the linear model due to the complex nature of the ocean environment. This presents a challenge in the transfer alignment of the ship. In order to enhance the precision and velocity of ship transfer alignment, as well as to streamline the alignment processes, this paper proposes a transfer alignment methodology based on the Earth-Centered Earth-Fixed (ECEF) frame special Euclidean group (SE(3)) matrix Lie group. After introducing the two navigation states, velocity and attitude, from the ECEF frame into SE(3), the nonlinear inertial navigation system error state model and its corresponding measurement equations are derived based on the mapping relationship between the Lie groups and Lie algebra. The method effectively solves the error problem due to linear approximation in the traditional transfer alignment method, and applies to misalignment angles of arbitrary scale. The simulation results verify the effectiveness and rapidity of the proposed alignment method in the case of arbitrary misalignment angles.

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%.

To address the uncertainty in the statistical distribution model of positioning error for the accuracy test of long-endurance inertial navigation systems, a probability distribution model adheres to the statistical rule of the radial positioning error of inertial navigation systems. The probability distribution density function (PDF), cumulative density function (CDF), and characteristic numbers (mean, standard deviation, root-mean-square) of the radial positioning error are derived based on the static-base positioning error of the long-range inertial navigation systems. Methods are provided for estimating the parameters of the probability distribution of the radial positioning errors. The theoretical derivation results demonstrate that the radial positioning error follows the Hoyt distribution. The distribution parameters and the number of features grow linearly with time, while the mean and standard deviation converge to 60% and 80% of the root-mean-square, respectively. Through a large-sample Monte Carlo simulation, the experimental results were consistent with the theoretical derivation results. These results indicate that the theoretical derivation results can be used to optimize the design of the long-endurance rotary modulation inertial navigation system’s accuracy test.

To address the urgent need for high-fidelity motion data for validating navigation algorithms for Unmanned Underwater Vehicles (UUVs), this paper proposes a data generation method based on a parametric motion model. First, based on the principles of rigid body dynamics and fluid mechanics, a decoupled six-degrees-of-freedom (6-DOF) Linear and Angular Acceleration Vector (LAAV) model is constructed, establishing a dynamic mapping relationship between the rudder angle and speed setting commands and motion acceleration. Second, a segmentation–identification framework is proposed for three-dimensional trajectory segmentation, integrating Gaussian Process Regression and Ordering Points To Identify the Clustering Structure (GPR-OPTICS), along with a Dynamic Immune Genetic Algorithm (DIGA). This framework utilizes real vessel data to achieve motion segment clustering and parameter identification, completing the construction of the LAAV model. On this basis, by introducing sensor error models, highly credible Inertial Measurement Unit (IMU) data are generated, and a complete attitude, velocity, and position (AVP) motion sequence is obtained through an inertial navigation solution. Experiments demonstrate that the AVP data generated by our method achieve over 88% reliability compared with the real vessel dataset. Furthermore, the proposed method outperforms the PSINS toolbox in both the reliability and accuracy of all motion parameters. These results validate the effectiveness and superiority of our proposed method, which provides a high-fidelity data benchmark for research on underwater navigation algorithms.