Aiming at the application demand in precise measurement of heave motion during ship movement, a heave motion measurement scheme based on inertial navigation system is proposed. In view of the poor adaptability of the traditional filtering method and the problem of phase shift and amplitude error, a new design of adaptive digital high-pass filter based on complementary method is proposed according to the prior knowledge and real-time sea condition monitoring results, which monitors the sea condition through a sliding monitoring window and adapts the filter parameters in real time. Twice integrations and three times of adaptive high-pass filtering experiments are carried out on the measured acceleration values, which can filter out the low-frequency system errors and retain the useful high-frequency heave motion. Simulations and experiments under different sea conditions verify the feasibility of the measurement scheme and the new filtering method. The test results show that, compared with the traditional method, the new filtering method can adjust the filter parameters adaptively according to the sea condition, and reduce the phase error and amplitude error of the heave motion, in which the amplitude error is reduced at least 10 times, the amplitude error is reduced at least 2 times, and the measurement precision reaches centimeter level.

The non-orthogonal angle of the single-axis rotational inertial navigation system can cause the attitude angle errors in horizontal plane. Aiming at the shortages that traditional least square method would bring out large approximation error when estimating the non-orthogonal angle of rotational axis and doesn’t consider the data’s statistic characteristics, a new correction method based on Kalman filter is introduced. On the basis of the traditional error model, the proposed method adds the accelerometer output as the observation, and then estimates the rotating-axis non-orthogonal angle and corrects the rotating-axis non-orthogonal error. Simulations results demonstrate that, with the new correction method, the horizontal attitude angles are reduced to 102 from 502, and the accuracy is improved by about 80%, which show that the precision of horizontal attitude angle is improved by the proposed method.

In the MEMS/GPS integrated navigation system, the positioning error of pure inertial navigation will quickly diverge during GPS outages. In order to suppress the error divergence, an improved radial basis neural network and an adaptive Kalman filter algorithm are proposed, and a new network training model is introduced. The adaptive quantum particle swarm optimization algorithm is used to improve the structural design and the parameters of radial basis neural network. The neural network is trained with the combined navigation data when the GPS signal is available. When the GPS outage occurs, the measurement of AKF is predicted by the improved radial basis neural network, so that the error of speed and position are provided continuously for the system. Experimental results show that when the GPS outage is up to 15 s, the horizontal positioning accuracy is improved by 62% compared with that of the original algorithm under the state of turning, which effectively suppresses the inertial navigation error.