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During underwater operations, divers must determine their own trajectories using the Inertial Navigation System (INS) they carry to improve operational efficiency. However, the INS contains a sensor bias that is also incorporated into the quadratic integration process to obtain the displacement, resulting in trajectory drift of the divers during prolonged self-guidance. To overcome the above problem, other aids are needed to correct the accumulated error of the INS. The single-beacon Assisted Inertial Navigation (AIN) method can improve the flexibility of inertial error correction while simplifying the localization equipment, which is suitable for the INS cumulative error correction scenario of divers. However, most of the traditional single-beacon assisted correction methods do not consider the effect of acoustic line bending on hydroacoustic ranging, and at the same time, they do not consider the problem of singular or pathological coefficient matrices introduced by inertial navigation neighbor localization deviations. Based on the above two shortcomings, this paper uses the acoustic velocity profile for acoustic line tracking, combines the localization idea of Mobile Primitives (MP), and proposes an MP-based acoustic line tracking-Assisted Inertial Navigation Localization (AINL) method, which constructs a sliding time window (STW) by taking the historical positioning of divers as a virtual primitive, and combines the nonlinear optimization method for iterative optimization search as a means to improve the accuracy and stability of self-navigation of the divers.

As ocean environment is complicated and varied, underwater vehicles (UVs) are facing great challenges in safe and precise navigation. Therefore, it is important to evaluate the underwater ocean environment safety for the UV navigation. To deal with the uncertain knowledge and various information in the safety assessment, we present an evaluation model based on the dynamic Bayesian network (DBN) theory. Firstly, characteristic indicators are extract from marine environment systems and discretized with Cloud model. Then, the DBN is constructed through structure learning and parameter learning based on Dempster-Shafer (DS) evidence theory. Finally, the dynamic evaluation and risk zoning of the navigation safety is realized based on Bayesian probabilistic reasoning. The DBN-based assessment model fully considers the uncertainty of influence relationships between marine environment and UV navigation, and effectively fuses expert knowledge and quantitative data for assessment modeling. The experimental results show the proposed model has high reliability and good value of application.

Maritime transportation is crucial to national economic development as it offers a low-cost, safe, and efficient alternative for movement of freight compared to its land or air counterparts. River and channel dredging protocols are often adopted in many ports and harbors of the world to meet the increasing demand for freight and ensure safe passage of larger vessels. However, such protocols may have unintended adverse consequences on flood risks and functioning of coastal ecosystems and thereby compromising the valuable services they provide to society and the environment. This study analyzes the compound effects of dredging protocols under a range of terrestrial and coastal flood drivers, including the effects of sea level rise (SLR) on compound flood risk, vessel navigability, and coastal wetland inundation dynamics in Mobile Bay (MB), Alabama. We develop a set of hydrodynamic simulation scenarios for a range of river flow and coastal water level regimes, SLR projections, and dredging protocols designed by the U.S. Army Corps of Engineers. We show that channel dredging helps increase bottom ('underkeel') clearances by a factor of 3.33 under current mean sea level and from 4.20 to 4.60 under SLR projections. We find that both low and high water surface elevations (WSEs) could be detrimental, with low WSE (< -1.22 m) hindering safe navigation whereas high WSE (> 0.87 m) triggering minor to major flooding in the surrounding urban and wetland areas. Likewise, we identify complex inundation patterns emerging from nonlinear interactions of SLR, flood drivers, and dredging protocols, and additionally estimate probability density functions (PDFs) of wetland inundation. We show that changes in mean sea level due to SLR diminish any effects of channel dredging on wetland inundation dynamics and shift the PDFs beyond pre-established thresholds for moderate and major flooding. In light of our results, we recommend the need for integrated analyses that account for compound

Compared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. More specifically, we generate multiple random trees from these sampling points, thereby bolstering both navigational accuracy and overall computational efficiency. Building upon these core techniques, we introduce a novel extension of the traditional rapidly-exploring random trees (RRT) connect algorithm-referred to as multiple RRT-connect (multi-RRT-connect)-aimed at swiftly determining a viable path between prescribed start and goal coordinates. As the number of samples expands, the random trees gradually enlarge and interlink, mirroring the functionality of classic RRT-connect and ultimately forming a continuous corridor. Subsequently, the derived path undergoes iterative refinement and optimization, culminating in a significantly reduced trajectory length.We subjected the proposed algorithm to rigorous testing through comprehensive simulations alongside meticulous comparisons with established state-of-the-art solutions. The results highlight the algorithm's distinct advantages across multiple dimensions such as path construction success, computational efficiency, and trajectory refinement quality, thereby underscoring its potential to advance autonomous navigation in challenging maritime settings.

With the global wave of intelligence and automation, ship autopilot technology has become the key to improving the efficiency of marine transportation, reducing operating costs, and ensuring navigation safety. However, existing reinforcement learning (RL)-based autopilot methods still face challenges such as low learning efficiency, redundant invalid exploration, and limited obstacle avoidance capability. To this end, this research proposes a GEPA model that integrates prior knowledge and hierarchical reward and punishment mechanisms to optimize the autopilot strategy for unmanned vessels based on deep Q-network (DQN). The GEPA model introduces a priori knowledge to guide the decision-making of the intelligent agent, reduces invalid explorations, and accelerates the learning convergence, and combines with hierarchical composite reward and punishment mechanisms to improve the rationality and safety of autopilot by means of end-point incentives, path-guided rewards, and irregular obstacle avoidance penalties. The experimental results show that the GEPA model outperforms the existing methods in terms of navigating efficiency, training convergence speed, path smoothness, obstacle avoidance ability and safety, with the number of training rounds to complete the task reduced by 24.85%, the path length reduced by up to about 70 pixels, the safety distance improved by 70.6%, and the number of collisions decreased significantly. The research in this paper provides an effective reinforcement learning optimization strategy for efficient and safe autonomous navigating of unmanned ships in complex marine environments, and can provide important theoretical support and practical guidance for the development of future intelligent ship technology.

The Northwest Passage is becoming navigable for longer periods of the year. Ship traffic, however, still bears hazardous risks. A German-Canadian research team co-initiated by FRAUNHOFER wants to change that. In the project PASSAGES, it is conducting the preparatory work for a safe navigation through the icy waters.

Underwater terrain-matching navigation technologies have become a popular topic for the high-precision positioning and navigation of autonomous underwater vehicles. This paper proposes an underwater terrain-matching positioning method based on a MARKOV random field model, which is based on real-time terrain data obtained using a multi-beam echo sounder. It focuses on the strong correlation between adjacent terrain data, which can improve terrain adaptability and matching accuracy. Playback simulation tests were conducted based on actual sea trial data, and the results showed that the proposed method has good positioning performance, which can correct the cumulative errors of inertial navigation systems. The results demonstrated the usability of the proposed method for positioning correction in underwater engineering applications.

In dense anchorage areas, the challenge of navigation for Unmanned Surface Vehicles (USVs) is particularly pronounced, especially regarding path safety and economy. A Risk-Aware Path Optimization Algorithm (RAPO) is proposed to enhance the safety and efficiency of USV navigating in anchorage areas. The algorithm incorporates risk assessment based on the A* algorithm to generate an optimized path and employs a Dual-Phase Smoothing Strategy to ensure path smoothness. First, the anchorage area is spatially separated using a Voronoi polygon, the RAPO algorithm includes a grid risk function, derived from the ship domain and Gaussian influence function, in the path evaluation criteria, directing USV to successfully bypass high-risk areas and as a result. Then the DPSS is used to decrease path turning points and boost path continuity, which in turn improves path economy. Simulation results demonstrate that this method significantly reduces the path length and the number of turning points, enhancing USV navigation safety in anchorage areas.

China Admiral: 'Freedom of Navigation Patrols' could end 'in disaster'.

China Admiral: 'Freedom of Navigation Patrols' could end 'in disaster'.

Godafoss captain admits mistakes Navigation.

US navy prepares allies to 'protect navigation' in Gulf.

This paper presents a geometric stochastic channel model designed for analyzing maritime communication and navigation services between moving ships using the C-band or sub-6 GHz spectrum, which aligns with the focus of emerging 5G networks on land. The channel model is validated through channel measurements conducted both on the sea and land. A software tool has been developed to integrate and analyze these measurements, which is included with this publication. The main challenge in developing the channel model for maritime services lies in the dynamic nature of the sea surface, leading to constantly changing reflection conditions due to varying reflectors and scatterers on the water. Additionally, the motion conditions of the transmitter and receiver on ships change in all three dimensions, depending on the sea state. To address these complexities, data from several measurement campaigns in diverse areas were collected. The analysis involved examining the propagation conditions over the sea with variations in sea surface roughness, antenna heights, and used bandwidths. Moreover, additional propagation conditions over nearby land were also taken into account. The study demonstrates that the changing antenna height on the ship, influenced by sea conditions, significantly affects the reflection and scattering conditions. The research aims to develop reliable, high-data rate, and broadband marine communication systems. Therefore, a measurement bandwidth of 120MHz was employed to derive the propagation model. This model not only offers absolute timing information but can also be used for time-based ranging or positioning systems. The proposed geometric stochastic channel model provides valuable insights into the complex maritime communication and navigation environment. By accounting for the continuously evolving sea surface and its impact on antenna height, the model offers a robust framework for studying and optimizing marine communication systems. The availability of

The U.S. military conducted 'freedom of navigation' operations against 13 countries last year, including several to challenge China's claims in the South and East China seas, according to an annual Pentagon report released on Monday.

Pakistan committed to ensuring freedom of navigation in Arabian Sea.

The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) is working on a satellite-based system for substantially improving ship navigation in ice-affected waters. The Earth observation satellites TerraSAR-X and TanDEM-X provide the high-resolution images needed to make this possible. Scientists from the Alfred Wegener Institute (AWI) - the Helmholtz Centre for Polar and Marine Research based in Bremerhaven - are currently on their way to Antarctica on board the research vessel 'Polarstern' to test the practicality of this technique.

Despite the availability of satellite navigation systems, and ships that are awash with electronics, maritime buoyage still matters, particularly in pilotage waters where visual aids provide the best possible way of marking a channel or identifying obstructions. These days, buoys can be "intelligent" in that they have radar reflectors to help them show up on ship radars, possibly fitted with electronic beacons that show up on electronic charts and even made individually identifiable through their own Automated Identification System signatures. Buoys still remain very useful indeed.

U.S. Coast Guard Deploys Drones to Inspect Aids to Navigation.

VTT Technical Research Centre of Finland is developing safe steering for the remote-monitored and controlled autonomous ships of the future. The new technology has been developed for navigation systems and ship autopilots, which steer ships automatically.

Researchers from the Finnish Geospatial Research Institute and Aalto University will team up with Fleetrange Ltd. and Tallink Grupp as part of a research project carried out under a programme of, and funded by, the European Space Agency (ESA). The goal is to develop techniques for autonomous navigation for ships with focus on safety, using a combination of different sensors, machine learning and artificial intelligence.

Denmark, South Korea to Cooperate on E-Navigation.