Search Results (185)

This study presents a deep learning–assisted integrated navigation scheme implemented on an autonomous underwater vehicle carrying a Chinese domestically developed strapdown inertial navigation system, designed for operation in surface and littoral environments. The system integrates measurements from SINS, the global positioning system, and a Doppler velocity log, while integrating a Decoder-based covariance estimator into the error state-extended Kalman filter. This hybrid architecture adaptively models time-varying processes and measurement noise from raw sensor inputs, greatly improving robustness for surface navigation in dynamic marine environments. To improve learning efficiency, we design a compact and informative feature representation that can be adapted to navigation error dynamics. The novel structure captures temporal dependencies and the evolution of nonlinear error more effectively than typical sequence models, achieving faster convergence and superior accuracy compared to GRU and Transformer baselines. The experimental results based on real sea trial data show that our method significantly outperforms model-based and learning-based methods in terms of navigation solution accuracy and stability, and the adaptive estimation of noise covariance. Specifically, it achieves the lowest RMSE of 0.0274, reducing errors by 94.6–34.6%, compared to conventional ES-EKF-integrated navigation, Transformer, GRU, and a DCE variant. These findings underscore the practical significance of integrating domain-informed filtering methodologies with deep noise modeling frameworks to achieve robust and accurate AUV surface navigation. Full article

This study addresses the nonlinear estimation problem in the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation by proposing an improved filtering algorithm based on $S{E}_2(3)$ Lie group state representation. A dynamic model satisfying the group affine condition is established to systematically construct both left-invariant and right-invariant error state spaces, upon which two nonlinear filtering approaches are developed. Although the fast unscented transformation method is not novel by itself, its first integration with the $S{E}_2(3)$ Lie group model for SINS/DVL integrated navigation represents a significant advancement. Experimental results demonstrate that under large misalignment angles, the proposed method achieves slightly lower attitude errors compared to linear approaches, while also reducing position estimation errors during dynamic maneuvers. The 12,000 s endurance test confirms the algorithm’s stable long-term performance. Compared with conventional unscented Kalman filter methods, the proposed approach not only reduces computation time by 90% but also achieves real-time processing capability on embedded platforms through optimized sampling strategies and hierarchical state propagation mechanisms. These innovations provide an underwater navigation solution that combines theoretical rigor with engineering practicality, effectively overcoming the computational efficiency and dynamic adaptability limitations of traditional nonlinear filtering methods. Full article

Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. To overcome these challenges, we propose a novel terrain-aided navigation framework integrating an Improved Marine Predators Algorithm with Depth-First Search optimization (DFS-IMPA-TAN). This framework maintains positioning precision in partially self-similar terrains through two synergistic mechanisms: (1) IMPA-driven optimization based on the hunger-inspired adaptive exploitation to determine optimal trajectory transformations, cascaded with Kalman filtering for navigation state correction; (2) a Robust Tree (RT) hypothesis manager that maintains potential trajectory candidates in graph-structured memory, employing Depth-First Search for ambiguity resolution in feature matching. Experimental validation through simulations and in-vehicle testing demonstrates the framework’s distinctive advantages: (1) consistent terrain association in partially self-similar topographies; (2) inherent error resilience against ambiguous feature measurements; and (3) long-term navigation stability. In all experimental groups, the root mean squared error of the framework remained around 60 m. Under adverse conditions, its navigation accuracy improved by over 30% compared to other traditional batch processing TAN methods. Comparative analysis confirms superior performance over conventional methods under challenging conditions, establishing DFS-IMPA-TAN as a robust navigation solution for AUVs in complex underwater environments. Full article

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries. Full article

Investigating the coupled hydrodynamic and thermal wakes induced by underwater vehicles is vital for non-acoustic detection and environmental monitoring. Here, the standard SUBOFF model is simulated under eight operating conditions—speeds of 10, 15, and 20 kn; depths of 10, 20, and 30 m; and both with and without thermal discharge—using Delayed Detached Eddy Simulation (DDES) coupled with the Volume of Fluid (VOF) method. Results indicate that, under heat emission conditions, higher speeds accelerate wake temperature decay, making the thermal wake difficult to detect downstream; without heat emission, turbulent mixing dominates the temperature field, and speed effects are minor. With increased speed, wake vorticity at a fixed location grows by about 30%, free-surface wave height rises from 0.05 to 0.15 m, and wavelength remains around 1.8 m, all positively correlated with speed. Dive depth is negatively correlated with wave height, decreasing from 0.15 to 0.04 m as depth increases from 5 to 20 m, while wavelength remains largely unchanged. At a 10 m submergence depth, the thermal wake is clearly detectable on the surface but becomes hard to detect beyond 20 m, indicating a pronounced depth effect on its visibility. These results not only confirm the positive correlation between vessel speed and wake vorticity reported in earlier studies but also extend those findings by providing the first quantitative evaluation of how submergence depth critically limits thermal wake visibility beyond 20 m. This research provides quantitative evaluations of wake characteristics under varying speeds, depths, and heat emissions, offering valuable insights for stealth navigation and detection technologies. Full article

The geometric distribution of seabed beacons significantly impacts the positioning accuracy of underwater acoustic navigation systems. To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). The method quantifies the contribution weights of each beacon to the dominant navigation direction by performing SVD on the acoustic observation matrix. The acoustic ranging covariance matrix can be dynamically adjusted based on these weights to suppress error propagation. At the same time, the prior depth with centimeter-level accuracy provided by the pressure sensor is used to establish strong constraints in the vertical direction. The experimental results demonstrate that the depth-constrained adaptive stochastic model optimization method reduces three-dimensional RMS errors by 66.65% (300 m depth) and 77.25% (2000 m depth) compared to conventional equal-weight models. Notably, the depth constraint alone achieves 95% vertical error suppression, while combined SVD optimization further enhances horizontal accuracy by 34.2–53.5%. These findings validate that coupling depth constraints with stochastic optimization effectively improves navigation accuracy in complex underwater environments. Full article

In this study, we improve the matching accuracy of underwater gravity-matching navigation and use this method to further analyze the confidence of the matching accuracy. An interquartile range (IQR)-matching approach based on Bayesian estimation, referred to as BEIQR, is proposed in this study. The method uses the correlation of the Terrain Contour Matching (TERCOM) algorithm as the a priori estimation and calculates the probability weights of the points to be matched by Bayesian a posteriori probability estimation. Additionally, it analyzes the distribution of the to-be-matched points to obtain the final matching results based on the accuracy requirements. Furthermore, a novel interquartile range confidence analysis method based on Bayesian estimation (BEIQRC) is proposed to assess the matching results. This method defines the matching point as the center and the accuracy requirement as the radius, analyzing the measurement weight and distance weight of the to-be-matched points within the accuracy circle. Based on this analysis, the final matching point is projected with the true position probability. The experimental results demonstrate that the proposed method is independent of the preorder matching results. By utilizing data from a single matching process, it effectively obtains the confidence of the matching results, providing a reliable reference for the accuracy assessment of gravity-matching outcomes. Full article

The dynamic and heterogeneous nature of marine environments, combined with severely constrained communication and energy resources, presents distinct challenges in constructing underwater Communication, Positioning, Navigation, and Timing (CPNT) systems compared to terrestrial Positioning, Navigation, and Timing (PNT) architecture. To address the inherent limitations of conventional decoupled CPNT systems – including high costs and low efficiency in communication and energy utilization – this study aims to propose a unified underwater CPNT ($U^{2}CPNT$) system that coordinates multi-modal data and resource allocation, thereby optimizing CPNT service performance in harsh underwater conditions. In this study, Cramér-Rao Lower Bound (CRLB) formalization is applied to theoretically analyze the feasibility of $U^{2}CPNT$ system, and the design of $U^{2}CPNT$ system is presented to realize the integrated design of CPNT. To validate the system performance, a real $U^{2}CPNT$ system was built and sea trials were conducted. With $U^{2}CPNT$ architecture, the integrated CPNT service can be provided, the positioning error is lower, the positioning continuity has improved by 7.68%, the velocity estimation error is less than 1 m/s, making $U^{2}CPNT$ a potential solution for underwater CPNT service. Full article

Autonomous Underwater Vehicle (AUV) navigation relies on bottom-tracking velocity from Doppler Velocity Log (DVL) for positioning through dead-reckoning or aiding Strapdown Inertial Navigation System (SINS). In mid-water environments, the distance between the AUV and the seafloor exceeds the detection range of DVL, causing failure of bottom-tracking and leaving only water-relative velocity available. This makes unknown ocean currents a significant error source that leads to substantial cumulative positioning errors. This paper proposes a method for mid-water ocean current estimation using multibeam bathymetric survey data. First, the method models the regional unknown current field using radius basis functions (RBFs) and establishes an AUV dead-reckoning model incorporating the current field. The RBF model inherently satisfies ocean current incompressibility. Subsequently, by dividing the multibeam bathymetric point cloud data surveyed by the AUV into submaps and performing a terrain-matching algorithm, relative position observations among different AUV positions can be constructed. These observations are then utilized to estimate the RBF parameters of the current field within the navigation model. Numerical simulations and experiments based on real-world bathymetric and ocean current data demonstrate that the proposed method can effectively capture the complex spatial variations in ocean currents, contributing to the accurate reconstruction of the mid-water current field and significant improvement in positioning accuracy. Full article

The global navigation satellite system (GNSS), using electromagnetic signals, enables continuous positioning throughout the entire surface of the Earth. However, underwater and underground environments significantly restrict the propagation of electromagnetic waves. The sole approach to aid positioning is the utilization of sound signals. Signal blockage in underground and indoor environments demands the accurate location of anchor points for local positioning, which requires previous deployment. Unlike radio waves, the cosmic ray muons are highly reliable natural signal sources for positioning, remaining immune to spoofing and interference. Starting from the standpoint of navigation and positioning, this paper briefly introduces the physical properties of cosmic ray muons and outlines the measurements and positioning principles of muon navigation, including trilateral localization based on the time of flight (TOF) and angular localization based on the angle of arrival (AOA). It subsequently presents the pertinent studies conducted and analyzes the findings. Finally, the challenges of muon navigation are explored from three perspectives: positioning signals, positioning models, and application scenarios. This will offer some new ideas for the domain of localization for further research on muon positioning. Full article

The development of underwater high-precision navigation technology is of great significance for the application of autonomous underwater vehicles (AUVs). Traditional long baseline (LBL) positioning systems require pre-deployment and the calibration of multiple beacons, which consumes valuable time and manpower. In contrast, the range-only single-beacon (ROSB) positioning technology can help autonomous underwater vehicles (AUVs) obtain accurate position information by deploying only one beacon. This method greatly reduces the time and workload of deploying beacons, showing high application potential and cost ratio. Given the operational constraints of AUV open-ocean navigation with single-beacon weak observations and absence of valid a priori positioning data in calibration zones, a multi-sensor underwater virtual beacon localization framework was established, proposing a differential Chan–Gauss–Newton (DCGN) methodology for submerged vehicles. Based on inertial navigation, the method uses the distance measurement information from a single beacon and observations from multiple sensors, such as the Doppler velocity log (DVL) and pressure sensor, to obtain accurate position estimates by discriminating the initial position of multiple hypotheses. A simulation experiment and lake test show that the proposed method not only significantly improves the positioning accuracy and convergence speed, but also shows high reliability. Full article

Currently, it is becoming relevant to use cheap autonomous underwater vehicles (AUVs) to perform various underwater operations (environmental monitoring, aquatic protection, search and tracking of underwater biological objects, etc.). At the same time, the main way to reduce the cost of AUVs is to reduce the number of expensive on-board acoustic sensors. But this leads to a decrease in the accuracy of determining the parameters of the movement of these AUVs and the difficulty of performing missions. To solve this problem, this paper proposes a new method for the synthesis of an AUV navigation system, which recovers unavailable information (due to the absence of an expensive sensor) based on the dynamic model of the AUV and its thruster control signals. At the same time, these estimates are corrected using AUV position information, which is generated by an external hydroacoustic station (HAS) and transmitted via acoustic communication channels. This approach does not require synchronization procedures between the AUV and the HAS, but its accuracy significantly depends on the accuracy of determining the AUV dynamic model and the parameters of underwater currents. Two new approaches are proposed to ensure the accuracy of the navigation system. The first approach is to use the Kalman filter to combine data obtained from different sources with different periods and to take into account delays in receiving AUV position information at the stage of correcting estimates made by the Kalman filter. The second approach is to more accurately estimate the parameters of the AUV model and underwater currents based on data on the trajectory of the AUV obtained from the HAS. The use of these refined parameters of the AUV dynamic model makes it possible to significantly increase the accuracy of the navigation system. The simulation results carried out take into account the characteristics of real on-board sensors of the AUV and the HAS, and acoustic data transmission channels showed the high accuracy of the proposed method of constructing a navigation system, which reduces the cost of creating AUVs. In addition, the proposed algorithm can also be used during the failure of a number of AUV on-board navigation sensors. Full article

The accurate prediction of the surfacing position of underwater gliders (UGs) is critical for mission success and cost-effective retrieval. However, current state-of-the-art (SOTA) methods often rely on complex multi-model integrations or large volumes of ocean current data, thereby increasing operational costs and system complexity. In this study, we systematically introduce—for the first time—a coordinate-transformation-based prediction framework, originally applied in other navigation contexts, into the UG surfacing-position-prediction task. By projecting both the glider’s entry and surfacing positions into a Universal Transverse Mercator (UTM) planar coordinate system and treating the resulting displacement as the prediction target, we avoid dependence on heavily parameterized current models, simplify the training process, and maintain robust predictive accuracy. Our approach combines common machine learning predictors (e.g., AdaBoost, LGBM, gradient boosting, random forest, decision trees) instead of advanced deep learning architectures, thus reducing computational overhead. Experiments on two real-world sea trial datasets (containing 2159 and 1456 profiles, respectively) show that, compared with direct regression approaches, this method improves positioning accuracy by up to 50% within a 500-meter range, yet requires minimal multi-source data. Overall, this study integrates the concept of coordinate transformation into the task of predicting the surfacing position of underwater gliders, effectively streamlining the method without sacrificing accuracy. The result is a highly flexible and cost-effective approach, providing theoretical support for future optimizations of underwater glider navigation systems. Full article

The remotely operated vehicle (ROV), a vital deep-sea platform, offers key advantages, including operational duration via continuous umbilical power, high task adaptability, and zero human risk. It has become indispensable for deep-sea scientific research and marine engineering. To enhance surveys of cobalt-rich crusts (CRCs) on complex seamount terrains, the 4500-m-class Haima ROV integrates advanced payloads, such as underwater positioning systems, multi-angle cameras, multi-functional manipulators, subsea shallow drilling systems, sediment samplers, and acoustic crust thickness gauges. Coordinated control between deck monitoring and subsea units enables stable multi-task execution within single dives, significantly improving operational efficiency. Survey results from Caiwei Guyot reveal the following: (1) ROV-collected data were highly reliable, with high-definition video mapping CRCs distribution across varied terrains. Captured crust-bearing rocks weighed up to 78 kg, drilled cores reached 110 cm, and acoustic thickness measurements had a 1–2 cm margin of error compared to in situ cores; (2) Video and cores analysis showed summit platforms (3–5° slopes) dominated by tabular crusts with gravel-type counterparts, summit margins (5–10° slopes) hosting gravel crusts partially covered by sediment, and steep slopes (12–15° slopes) exhibiting mixed crust types under sediment coverage. Thicker crusts clustered at summit margins (14 and 15 cm, respectively) compared to thinner crusts on platforms and slopes (10 and 7 cm, respectively). The Haima ROV successfully investigated CRC resources in complex terrains, laying the groundwork for seamount crust resource evaluations. Future advancements will focus on high-precision navigation and control, high-resolution crust thickness measurement, optical imaging optimization, and AI-enhanced image recognition. Full article

The development and utilization of marine resources by humanity are increasing rapidly, and a single unmanned underwater vehicle (UUV) is insufficient to meet the demands of ocean exploitation. Large-scale UUV swarms present a primary solution; however, challenges such as underwater mountain ranges and signal attenuation critically impact the real-time collaborative positioning and autonomous clustering abilities of these swarms, posing major issues for their practical application. To address these challenges, this paper proposes a UUV cluster distributed navigation fusion positioning method with information geometry (UCDFP). This method transforms the navigation data of individual UUVs into an information geometric probability model, thereby reducing the impact of temporal asynchrony-induced positioning errors. By integrating factor graph theory and utilizing ranging information between UUVs, a distributed collaborative fusion positioning architecture for UUV swarms is established, enabling seamless dispersion and regrouping. In experimental evaluations, the proposed method is compared with existing techniques concerning convergence speed and the capability of UUV swarms for autonomous dispersion and regrouping. The results indicate that the method proposed in this paper achieves faster convergence and higher positioning stability during the autonomous clustering of UUV swarms, marking a notable advancement in underwater vehicular technology. Full article