Multi-satellite and Multi-Signal-to-Noise Ratio (SNR) types provide more basic data for the monitoring of sea level height by Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology. Few studies predict sea level height with high temporal resolution. This study proposes a theory based on GNSS-IR technology and Auto Regressive Integrated Moving Average (ARIMA) model. Taking the MAYG station as an example, the process of sea level height prediction and the determination of the best prediction scheme are shown. First, the sea level height monitored by the GNSS-IR technology is averaged hour by hour as the sea level height at the instantaneous moment. A linear interpolation method is used to compensate for the missing sea level time series. Then, the number of days to participate in the prediction of GNSS-IR sea level height and its forecast are selected for the experiment of predicting tide level. In the tide level prediction study from DOY125 to DOY153, the results of predicting the future 24 h, 48 h, and 72 h from one tide level period (29 days) illustrate that the accuracy of predicting the sea level height at 24 h is the best (R: 0.967, RMSE: 0.270 m, ME: −0.151 m), and the predicted tide level is highly consistent with the tide level measured at the tide gauge station. In addition, experiments reveal that the prediction accuracy gradually decreases with the increase of prediction time. This study verifies the feasibility of the hour-by-hour sea level prediction theory combined with the GNSS-IR technology and the ARIMA model. It proves that the theory can carry out continuous prediction of sea level, further promoting the breakthrough and transformation of GNSS-IR technology to the application level.

Currently, integrated navigation systems with the inertial navigation system (INS)/geomagnetic navigation system (GNS) have been widely used in underwater navigation of autonomous underwater vehicle (AUV). Restricting AUV to navigate in the geomagnetic suitability areas (GSA) as far as possible can effectively improve the accuracy of integrated navigation systems. In order to improve the classification accuracy of GSA, a new optimal classification method based on principal component analysis (PCA) and improved back propagation (BP) neural network is proposed. PCA is used to extract the independent characteristic parameters containing the main components. Then, considering similarity coefficient, the initial weights and thresholds of BP neural network is optimized by improved adaptive genetic algorithm (IAGA). Finally, the correspondence between the geomagnetic characteristic parameters and matching performance is established based on PCA and improved adaptive genetic algorithm and back propagation (IAGA-BP) neural network for the automatic recognition of GSA. Simulated experiments based on PCA and IAGA-BP neural network shows high classification accuracy and reliability in the GSA selection. The method could provide important support for AUV path planning, which is an effective guarantee for AUV high precision and long voyage autonomous navigation.

Integration of strapdown inertial navigation system (SINS) and doppler velocity log (DVL) is usually applied in underwater applications, wherein DVL provides the three-dimensional velocity, and hence the accumulated error of SINS can be compensated. However, the DVL/SINS loosely coupled approach fails in the complex environments on the condition of fewer than three available beams. And the tightly coupled approach divergences with only one direction velocity information from the beam measurement. To solve the problems, a novel tightly coupled method is proposed in this paper, in which the state of UUV motion is considered, and a robust adaptive Kalman filter is utilized to dynamically estimate the observation noise. Experiment results indicate that the proposed method is efficient in UUV missions for beam limited environment.

The tightly coupled GNSS precise point positioning (PPP) and inertial navigation system (INS) can provide high-precision position, velocity and attitude information. The coupled system utilizes single receiver, which is particularly suitable for the environment without reference station, such as marine survey. In the former works, the integer ambiguity resolution of PPP/INS in terrestrial environment is researched. However, the GNSS observation are severely affected by the multipath effect in marine environment. In addition, the sideslip caused by wind and sea wave also impact float ambiguity estimation, consequently introducing difficulty for PPP ambiguity fixing. Therefore, the PPP/INS tightly coupled model with fixed ambiguity is proposed for marine survey. The correction model of INS gyroscope bias in closed-loop is deduced in detail. The influence of ship motion noise and multipath in marine environment is reduced by introducing the robust factor to the Kalman filter. The feasibility of the method is verified in a real marine experiment, with a detail evaluation of the data quality and positioning accuracy. The results show that the accuracy of PPP/INS can reach centimeter level after fixing the ambiguity in marine environment. Furthermore, the precise INS-predicted position can significantly shorten the re-fixed time of PPP/INS, which prove the efficiency of the proposed approach.