The prerequisite for underwater vehicles to accomplish various underwater tasks is to obtain real-time position information about themselves in the underwater environment using internal or external sensors, which is known as underwater positioning and navigation technology. This paper presents a general review of underwater vehicle positioning and navigation technology and illustrates the importance of underwater terrain-aided navigation technology. It also summarizes its general structure and describes the principles and functions of each component. The paper mainly analyzes various mainstream methods applied to underwater terrain-aided navigation, focuses on terrain suitability analysis and terrain matching algorithms, which play a central role in the analysis. Additionally, the research progress of terrain-aided navigation technology is described, and the research methods of terrain suitability analysis, terrain matching navigation, and the latest technology applications are compared. Some shortcomings of the current underwater terrain-aided navigation technology are presented, and future research directions are foreseen to provide a basis for subsequent research.

Underwater mobile acoustic source target localization encounters several challenges, including the unknown propagation speed of the source signal, uncertainty in the observation platform’s position and velocity (i.e., platform systematic errors), and economic costs. This paper proposes a new two-step closed-form localization algorithm that jointly estimates the angle of arrival (AOA), time difference of arrival (TDOA), and frequency difference of arrival (FDOA) to address these challenges. The algorithm initially introduces auxiliary variables to construct pseudo-linear equations to obtain the initial solution. It then exploits the relationship between the unknown and auxiliary variables to derive the exact solution comprising solely the unknown variables. Both theoretical analyses and simulation experiments demonstrate that the proposed method accurately estimates the position, velocity, and speed of the sound source even with an unknown sound speed and platform systematic errors. It achieves asymptotic optimality within a reasonable error range to approach the Cramér–Rao lower bound (CRLB). Furthermore, the algorithm exhibits low complexity, reduces the number of required localization platforms, and decreases the economic costs. Additionally, the simulation experiments validate the effectiveness of the proposed localization method across various scenarios, outperforming other comparative algorithms.

In this study, the drag coefficient and lift-to-drag ratio variation with angle of attack and velocity are analyzed by numerical simulation of the hydrodynamics of the initial shape of an autonomous underwater vehicle (AUV). Based on this, the response surface method (RSM) and multi-objective genetic algorithm (MOGA) are used to optimize the geometric parameters of the shape, aiming to improve the lift-to-drag ratio and reduce the mass. In the study, a second-order response surface model was constructed to analyze the relationship between the target variables and the structural geometric parameters, and the MOGA algorithm effectively searched for the globally optimal solution. The optimization results show that the lift-to-drag ratio is increased from 0.684 to 0.778 and the mass of the shell is reduced from 26.6 kg to 24.06 kg, which significantly improves the hydrodynamic performance of the AUV. The optimization method not only improves the performance of the AUV, but also provides a valuable reference for its hydrodynamic design, which has a good application prospect.

Terrain matching is a core component of underwater terrain-aided navigation system, which determines the accuracy of the underwater vehicle’s localization. Traditional terrain matching methods are lacking in improving the matching performance by extracting effective terrain features, and the effects of sample noise and rotational changes in the measurement data on the matching precision and matching accuracy are not well addressed. This paper proposes a deep learning method for extracting global style and local detail terrain features, which yields terrain feature mappings with enhanced representational capabilities. Meanwhile, without using any sample labels and negative samples, we propose an end-to-end learning framework that combines self-distillation and contrastive learning to achieve characterization learning for two augmented samples of the same terrain data. To improve the model’s ability to resist sample noise and rotational variations, we implement data augmentation by simulating measurement noise and rotational variations in underwater topographic measurements. In addition, we achieved high-precision terrain matching by comparing the abstract representations between samples rather than the terrain elevation values themselves. We have done a large number of comparison experiments with other methods, and the results show that our method has gained superior matching performance. The proposed method also enables high-precision matching of terrain data with varying sizes and resolutions, addressing the issue of limited high-resolution terrain data for training in real-world applications. Simulation test results show that our proposed method can obtain good localization accuracy in both rough and flat areas with good robustness.

Underwater terrain-aided navigation system can effectively correct the drift error of inertial navigation system that gradually increases with the accumulation of time, among which, underwater terrain suitability analysis is an important part of underwater terrain-aided navigation system. Traditional approaches view terrain matching and terrain suitability analysis as separate processes, which leaves terrain suitability analysis devoid of the knowledge used through navigation and terrain matching in particular. Whereas terrain matching can often provide useful information for TSA. This study attempts to use a deep learning model to deal with the terrain matching problem and terrain suitability analysis problem in the dimension of abstract features using a unified feature encoder. We propose a framework for sharing knowledge during the terrain analysis and matching phases, and construct a fitted probabilistic nonlinear classification network for matching. We validate the effectiveness of the method by performing suitability analysis in terrain regions with different geomorphic differences. After that, the good performance of the method is further verified by the navigation results in regions with different suitability. Finally, the feasibility and effectiveness of the terrain-assisted navigation system proposed in this paper are verified and analyzed as a whole in the form of semi-physical simulation.

The parameter estimation of multiple frequency-hopping (multiple FH) signals with frequency-switching time is a great challenge under conditions in which the number of signals is unknown. Due to the increasing mobility of devices such as unmanned aerial vehicles (UAVs), speed of parameter estimation is even more demanding. To solve this problem, an algorithm for estimating parameters of multiple FH signals based on compressed spectrum sensing and maximum likelihood (CSML) theory is proposed in this paper. First, the received signal is split into segments of the same length, and the frequencies contained in each segment are extracted using compressed spectrum sensing and kurtosis threshold. Next, the frequencies contained in adjacent segments are compared to find the signal segment in which frequency hopping occurs and its corresponding frequency. Finally, a hopping-time fast estimation algorithm based on the maximum likelihood theory is used to estimate the hopping time. Simulation results show that the algorithm proposed in this paper can estimate the parameters of multiple FH signals quickly and accurately when the number of signals is unknown and that it is equally effective for multiple FH signals with frequency-switching time.