A new robust strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation algorithm are proposed in this paper with a focus on suppressing the process uncertainty and measurement outliers induced by severe manoeuvering. In the proposed algorithm, the one-step predicted probability density function is modeled as Student’s t-distribution to deal with the heavy-tailed process noise, and hierarchical Gaussian state-space model for SINS/DVL integrated navigation algorithm is constructed. To detect and eliminate the measurement outliers, each measurement is marked by a binary indicator variable modeled as a beta-Bernoulli distribution. The variational Bayesian approach is used to jointly estimate state vector, auxiliary random variable, scale matrix, Bernoulli variable, and beta variable. The experimental results illustrate that the proposed algorithm has better robustness and navigation accuracy to deal with process uncertainty and measurement outliers than existing state-of-the-art algorithms.

The grid strapdown inertial navigation system (SINS) used in polar navigation also includes three kinds of periodic oscillation errors as common SINS are based on a geographic coordinate system. Aiming ships which have the external information to conduct a system reset regularly, suppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy. The Kalman filter based on the grid SINS error model which applies to the ship is established in this paper. The errors of grid-level attitude angles can be accurately estimated when the external velocity contains constant error, and then correcting the errors of the grid-level attitude angles through feedback correction can effectively dampen the Schuler periodic oscillation. The simulation results show that with the aid of external reference velocity, the proposed external level damping algorithm based on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with the traditional external level damping algorithm based on the damping network, the algorithm proposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from the non-damping state to the damping state, and this effectively improves the navigation accuracy of the system.

An improved coarse alignment (ICA) algorithm is proposed in this paper with a focus on improving alignment accuracy of odometer-aided strapdown inertial navigation system (SINS) under variable velocity and variable acceleration condition. In the proposed algorithm, the outputs of inertial sensors and odometer in a sampling interval are linearized rather than assumed to be a constant, which improves the accuracy of the vector observations and the precision of coarse alignment. Simulation and field test results illustrate that, under variable velocity and variable acceleration condition, the proposed algorithm can obtain a better alignment performance than conventional coarse alignment method.

Due to the influence of underwater acoustic multipath channel and lack of prior information, the likelihood-based MPSK modulation identification method is facing great challenges. Therefore, we proposed an identification method combining the orthogonal matching pursuit (OMP) and expectation maximization (EM) in single input and multiple output (SIMO) underwater acoustic channel. First, the blind channel is estimated and equalized by the OMP. Then, the EM is used to compensate the recovered constellation. Finally, the likelihood-based method is applied to identify the MPSK modulation. Numerical results show the proposed method can achieve more than 95% identification rate while the SNR is higher than 7 dB.

A comprehensive calibration algorithm is proposed in this paper with the aim of solving the problem whereby the navigation error of a long-endurance shipborne grid strapdown inertial navigation system (SINS) drifts with time. First, the equation within the inertial frame is deduced to establish the relationship between the position error, the grid yaw error and the platform drift angle in the inertial frame; then in combination with the equation within the inertial frame, the calibration schemes are designed. The gyroscope drift in the body frame is estimated and compensated through the designed calibration schemes with the aid of information either on two intermittent external positions and yaw, or on three intermittent external positions. The simulation results illustrate that, in the former case, the three-axis gyroscope drifts can be estimated accurately; in the latter case, the z -axis gyroscope drift and the grid yaw error can be estimated accurately. Resetting the system error based on external navigation information and compensating the gyroscope drift can effectively restrain the accumulated navigation error of shipborne grid SINS; meanwhile the proposed algorithm is unaffected by the motion of the ship, which has significant practical value in engineering.