Accurate prediction of surface ocean transport is critical for applications such as search and rescue, oil spill response, and marine hazard mitigation. We present a new tool developed to evaluate and compare ocean surface trajectory estimates from multiple numerical models using high-frequency radar (HFR) derived surface currents and in situ drifter data as observational references. The tool supports model intercomparison by aligning model output with virtual drifter release points and computing trajectory skill metrics including separation distance after 12 and 25 hours as well as a Lagrangian skill score. This framework has been applied in the Mid-Atlantic Bight from October 2024 to present using data from recent deployments of surface drifters and regional HF radar measurements. Forecasts from an ensemble of four ocean modeling systems-Short Term Prediction System (STPS), Regional Ocean Modelling System (ROMS) Doppio, Copernicus Marine Environment Monitoring Service (CMEMS), and the Earth System Prediction Capability (ESPC)-were evaluated over a range of oceanographic conditions. Results reveal variability in model performance, with some models excelling in surface drift assessment after 25 hours. HF radar data provided highresolution surface current fields served as an independent, synoptic benchmark across all models. This modular, open-source tool enables real-time and retrospective model evaluation, supports operational decision-making, and can be integrated into other ocean observing systems. Ongoing development aims to extend functionality to include uncertainty quantification, multimodel ensemble weighting, and support for additional observational datasets.
This paper presents a low-cost and scalable localization framework for autonomous underwater vehicles using one-way acoustic communication in shallow water environments. The system relies solely on received signal strength (RSS) from four fixed acoustic beacons and a heading measurement to estimate the vehicle's position. The signal field of each beacon is modeled independently using Gaussian Process regression, enabling a spatially-smooth data-driven representation of the signal landscape, even in environments prone to severe multipath effects. Motion is sampled probabilistically based on commanded velocity and heading measurements, then fused with RSS observations using a particle filter. Unlike conventional acoustic positioning systems that require synchronized two-way communication, our approach leverages one-way broadcasts and passive localization of the agent, reducing system complexity and enabling scalable multi-agent deployments without added infrastructure cost. Experimental results demonstrate sub-20 meter localization accuracy, outperforming a bearing only localization approach and highlighting the viability of RSS-based acoustic localization in shallow, cluttered, GPS-denied marine environments.
This study proposes a Grey-box system identification framework for Unmanned Surface Vehicles (USVs) operating under complex environmental disturbances. Planar motion dynamics (surge, sway, and yaw) of a low-speed flat-bottom USV are explicitly derived from physical principles and augmented with propulsion and wind effects. These dynamics are embedded in a Nonlinear AutoRegressive with eXogenous inputs (NARX) model, where the system accelerations are decoupled and formulated in terms of measurable variables and identifiable parameters. A custom USV testbed for experiment was developed and instrumented with an inertial navigation system, a data acquisition system, and a weather station. Sensor data were synchronized at 100 Hz and preprocessed to remove noise and ensure consistency. Field experiments under various maneuvers and environmental conditions demonstrated high prediction accuracy of the proposed model. However, its performance exhibited moderate sensitivity to the tuning of parameters within the nonlinear output function. Future research will focus on enhancing robustness and adaptability under broader operational scenarios.
Sea surface temperature (SST) and chlorophyll-a (Chl-$a$) are key indicators of ecosystem variability in temperate coastal-open ocean environments, reflecting both physical and biological responses. This study investigates the spatiotemporal variability of SST and Chl-$a$ in Sagami Bay using level 2 ocean color satellite data from Second generation Global Imager (SGLI)/Global Change Observation Mission-Climate (GCOMC) from 2018 to 2024. To address substantial data gaps caused by frequent cloud cover and sensor limitations such as sun glint, atmospheric interference, and land adjacency effects, we reconstructed missing values using the Data INterpolating Empirical Orthogonal Functions (DINEOF) method. Validation against randomly masked original satellite pixels and in-situ measurements confirmed that DINEOF shows significant correlation in filling gaps in both SST band Ch-$a$. Empirical Orthogonal Function (EOF) analysis was conducted on the DINEOF-reconstructed monthly datasets to extract dominant spatiotemporal modes of variability. SST showed strong seasonal signals in the leading EOF mode, with subsequent modes reflecting mesoscale variability, including Kuroshio intrusions. EOF modes of Chl-$a$ revealed seasonal bloom patterns and spatially complex responses to physical forcing, particularly in nearshore and river-influenced areas. The results highlight the importance and effectiveness of satellite data gap-filling for capturing short-term events and mesoscale dynamics in coastal waters. Our findings provide an insight into the spatiotemporal variability of physical and biological parameters in Sagami Bay and demonstrate a scalable approach for monitoring coastal systems under environmental change.
The Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) and the Long-Term Borehole Monitoring System (LTBMS) have been deployed along the Nankai Trough to monitor seismic activity, crustal deformation, and tsunamis in real-time. Accurate temperature measurement is critical for calibrating hydraulic pressure sensors within these systems, as sensor readings can be affected by heat generated from electrical power consumption and environmental variations. In this study, we evaluated the reliability of DONET and LTBMS temperature sensors by comparing their measurements with reference temperature data obtained from CTD sensors deployed by the AUV-NEXT near the D-node. Results indicate that both oscillator temperature (OYT) and external thermometers (LKO) are influenced by electrical power heat, with DONET and LTBMS sensors recording higher temperatures than the reference CTD measurements despite being at greater depths. Additionally, we estimated the spatial distribution of seafloor electrical conductivity and derived seafloor temperatures independent of electrical heating effects, providing a reference for further calibration. The findings highlight the importance of accounting for thermal influences in long-term oceanographic and geophysical monitoring using subsea networks. Future work will focus on classifying long-term temperature variations into components attributable to global ocean warming and oceanic processes, such as sea surface height changes and baroclinic responses affecting seafloor temperature and pressure measurements toward precise monitoring of seafloor crustal deformation.
Spiking Neural Networks are recognized as the third-generation neural networks. They are biologically inspired and energy efficient, making them suitable for underwater data analysis tasks. A common implementation of SNNs is the leaky integrate-and-fire, or LIF, neuron, which integrates input over time. Another implementation, proposed by Eugene M. Izhikevich, uses intrinsic parameters to control the neuron and its reset. Both models can have limitations in regression tasks, however, limiting accuracy. In this paper, we proposed a Hybrid model, combining aspects of the LIF and Izhikevich neuron. Experimental results on oceanographic data found that our proposed method achieves competitive accuracy and improves CPU time compared to the baseline models. The Hybrid model has lower L1 loss compared to existing models, which means that the hybrid model is good at predicting sparse oceanographic data. Future work on this model could include introducing adaptivity to the tuning parameter.
Cobalt-rich manganese crust (Mn-crust) is a deep sea mineral resource being targeted as a source of critical minerals. Estimating the volumetric distribution of Mn-crust requires measuring its thickness and lateral distribution. Acoustic in-situ methods are proven to be effective for continuous thickness measurements suitable for volumetric estimation, whereas visual surveys have been used for identifying Mn-crust in order to calculate lateral distribution. This paper expands on the previous studies by analyzing the suitability of acoustic, visual and parameter fusion methods for improving seafloor classification. Using two field collected datasets from distinct seafloor areas with different characteristics, performance of feature selection and classification methods are discussed.
This paper presents a comprehensive framework for integrating the Internet of Military Things (IoMT) into multi-domain cybersecurity and cyber defense training environments, with a particular focus on enhancing global maritime navigation security. The proposed architecture addresses current limitations in cyber training by combining physical and virtualized IoMT assets, federated cyber ranges, and advanced technologies such as AI-driven scenario orchestration, federated learning, and blockchain-based trust management. The framework supports realistic, scalable, and interoperable simulations across cyber, maritime, and space domains, enabling dynamic scenario adaptation and cross-domain event propagation. Functional and technical requirements are defined to ensure high-fidelity emulation, secure data exchange, and effective participant assessment. A prototype implementation is planned to validate the framework through multi-domain exercises, including scenarios involving GPS spoofing and satellite jamming. The outcomes are expected to significantly advance cyber defense capabilities, operational readiness, and innovation in cyber range design, with applications extending to both military and civilian sectors.
The Crabster Robot for Mine disposal (CRM) is a hexapod underwater walking platform developed to detect and neutralize seabed mines by navigating across the ocean floor using six articulated legs and four integrated thrusters. A buoyancy control mechanism based on a compressed air system enables seamless transition between walking and propulsion modes. One of the principal challenges in subsea operation is maintaining dynamic stability under hydrodynamic disturbances, particularly the risk of tumbling. To address this, we propose an extended dynamic tumble stability margin tailored for underwater locomotion, adapted from methods originally designed for terrestrial quadrupeds. Under the assumption of a stationary robot on a flat seabed, we evaluate the influence of buoyancy and fluid dynamics on overall stability. Our findings demonstrate posture-dependent stability, with lateral flows posing less risk of tumbling than frontal currents. These results provide insights into robust operation strategies for underwater walking robots in hazardous environments.
The University of Southern Mississippi (USM) has been dependent on acoustic tracking and communication ever since beginning AUV and ROV operations in the early 2000s. Since this time, the vehicles team has worked to implement operational improvements and navigational corrections, often with affordable acoustic technologies fitting within tight program budgets. This resourcefulness led to early multi-AUV operations to survey more efficiently. Recent upgrades to modern tracking and communications technology have allowed streamlined single-vehicle operations detailed in this paper. The greatest advancement has been improving the quality of vehicle navigation. Error has been reduced through post-processing and in-mission aiding of the vehicle navigation system with acoustically-derived positioning. The opportunities provided by these advancements led USM to propose novel multi-vehicle concepts, returning to operational ideas they helped pioneer over ten years ago.
The Barnacle is a new form of marine turbulence sensor, which has been shown to work as well as, or better than, acoustic devices when rigidly-mounted. In order to undertake more ambitious oceanographic measurements, it needs to be mounted on an Unmanned Underwater Vehicle (UUV). This paper aims to quantify two challenges associated with mounting the Barnacle on a UUV. First, the Barnacle relies on pressure measurements from five faces at different angles to each other. If mounted in the vicinity of another, larger object, these pressure measurements will be distorted. This distortion must be quantified, so that steps can be taken to mitigate it or incorporate it into the velocity calibration process. Second, changes in ambient temperature will cause drift in the pressure transducers used. The Barnacle has inbuilt temperature monitoring, and so an algorithm is required to use this temperature data to remove error from the velocity measurements. In this work, a Barnacle probe was mounted on a mock-up of an ecoSUBm5 nosecone and the influence of the nosecone on the Barnacle calibration was measured at different displacements. It is shown that the Barnacle needs to be one UUV nosecone diameter upstream to avoid the errors becoming large. Furthermore, a rolling regression algorithm is shown to remove errors due to temperature changes. This new understanding paves the way for further trials with the Barnacle mounted on a UUV.
The Python hfr-drifters toolbox is a conversion of a MATLAB toolbox designed to analyze and visualize ocean surface velocities by comparing drifter-derived velocities against measurements from High Frequency (HF) Radars. Drifters provide in-situ measurements for comparison with HF Radars. The original MATLAB toolbox (hfr-drifters) was created for validating Rutgers owned HF radar systems by assessing both an individual site's radial velocity and the networks combined total velocity accuracy. This conversion seeks to modernize the toolbox by making it accessible in the widely adopted programming language Python, while also introducing new features and enhanced flexibility for data output. The MATLAB toolbox and Python conversion contribute to the National Oceanic and Atmospheric Administration's (NOAA) initiative to advance Quality Assurance Real Time Oceanographic Data (QARTOD) standards. The toolbox incorporates quality control methods such as velocity rate-of-change, standard deviation, and positional distance checks (or peak tests), ensuring accurate and reliable data comparisons on the radial and total velocity data. Additionally, the toolbox provides analytical capabilities for assessing total velocity products, which integrate radial velocity data from multiple radar sites into comprehensive surface current maps. Visualization capabilities of the toolbox include time series plots comparing drifter velocities with HF radar radials and totals, scatter plots displaying velocity correlations, geospatial visualizations of drifter trajectories with velocity vectors, and current roses which offer insights into directional and speed distributions. Rutgers researchers utilize these insights to verify radar data quality and support ongoing research and projects. Converting the MATLAB toolbox to Python creates pathways to integrate numerous other oceanographic packages and libraries into the analyses while also making the validation methodology more available to the community.
Reliable underwater acoustic measurements depend heavily on the precise calibration of transducer systems. This work introduces a novel calibration strategy tailored for spiral acoustic sources composed of multiple vibrating segments. Spiral sources are used to estimate the bearing angle by computing the phase difference between the produced spiral field and a reference/circular field. Departing from conventional methods that apply amplitude adjustment during emission and phase correction during reception, the proposed technique performs phase calibration at the transmission stage. This enables the spiral source to function as a localization beacon for arbitrary underwater devices, independent of any prior phase configuration knowledge. A finite element method (FEM) model was developed to characterize the behavior of individual spiral source quadrants and to support the development of the system's data model. The calibration method assumes that the spiral source behaves as a linear system, generating a circular acoustic field with constant phase distribution along its azimuth. The method involves determining optimal amplitude and phase excitation values for each quadrant to produce the desired spiral field phase. Experimental validation in a controlled underwater setup showed a notable decrease in maximum phase error from 12.6 degrees to 3.3 degrees. Furthermore, FEM and experimental results indicated that the channel impulse response (CIR) estimates were altered by the transmitting voltage responses (TVRs) in both circular and spiral modes. The proposed TVR correction method was experimentally validated and demonstrated to be effective, indicating its potential for future integration into calibration procedures.
The convergence of high-resolution sensing and reliable communications is the cornerstone of next-generation ocean monitoring systems. Underwater acoustic channels are notoriously hostile due to severe multipath, long delay spreads and frequency-selective fading. Orthogonal chirp division multiplexing (OCDM) has recently emerged as a robust modulation for such channels because the Fresnel transform effectively diagonalizes the convolutional channel. In this work the received OCDM block is equalized in the frequency domain using a zero-forcing ($\mathbf{Z F}$) filter, which removes both the channel and the per-subcarrier power weighting and yields a constant output signal-to-noise ratio (SNR) across chirp subcarriers. In parallel, integrated sensing and communication (ISAC) has become a key research direction for 6 G networks, promising spectrum-efficient joint sensing and communication functionality. Yet the combination of OCDM with ISAC in underwater environments remains largely unexplored. This paper presents an OCDM-based underwater ISAC framework that incorporates a novel diagonal power-allocation matrix $D_{p}$ to weight each chirp subcarrier and formulates a power-allocation problem that maximizes a weighted sum of the communication and sensing SNRs. Closed-form expressions for both SNRs are derived: the communication SNR after ZF equalization equals the harmonic mean of the per-subcarrier SNRs, whereas the sensing SNR is a weighted sum of the allocated powers. We show that the resulting weighted optimization reduces to a convex program in the power variables and derive a square-root water-filling solution. Numerical simulations demonstrate that our design offers a flexible trade-off between communication and sensing performance under realistic acoustic channels and outperforms equal-power allocation benchmarks.
The present study characterized four optical water types (OWTs) in Sagami Bay based on in situ normalized remote sensing reflectance $\left[R_{\text{rs}}(\lambda)\right]$. Each OWT showed a distinct spectral shape and peak wavelength, and the 490 $\text{nm} / 565 \text{nm}$ band ratio differed significantly among all types ($P <0.05$). Phytoplankton community structure also varied significantly among OWTs (pairwise ANOSIM, $P<0.05$), suggesting that OWT classification may be useful for estimating community composition. In addition, we evaluated the possibility to identify these OWTs using satellite-derived $\boldsymbol{R}_{\text{rs}}(\lambda)$. Satellite-derived $R_{\text{rs}}(\lambda)$ values correlated significantly with in situ $R_{\text{rs}}(\lambda)$ only at $490,530,565$, and 670 nm, indicating that spectral shape alone is insufficient for accurate OWT identification. Nevertheless, the $490 \text{nm} / 565 \text{nm}$ band ratio from satellite-derived $R_{\text{rs}}(\lambda)$ differed significantly among OWTs IIIV. These results suggest that satellite observations can still support partial identification of OWTs within the bay.
Derelict Dungeness crab pots are a widespread and costly form of marine debris in Puget Sound, where they continue to trap marine life long after being lost. Traditional removal efforts rely on commercial divers, but diver-based methods are limited by depth and availability of trained personnel. This study presents a practical framework for derelict pot recovery using Remotely Operated Vehicles (ROVs), evaluating the method's efficiency, accessibility, and potential for community-led implementation. Here we describe the findings of retrieval operations between 2021 and 2025, with a case study focused on a 2023 effort in Sequim Bay, Washington. Using a low-cost modified BlueROV2, 50 pots were successfully removed from depths greater than the 100-foot depth limit for dive removal efforts. These results show that ROV-based removal offers a scalable, lowrisk complement to diver-based methods, with the potential to be employed by the tribal fishing community and youth-led STEAM programs.
Recent maritime incidents highlight the need for scalable underwater monitoring solutions. Traditional methods of ocean exploration face high costs and limited access, while collaborative teams of Autonomous Underwater Vehicle (AUV)s offer a promising alternative. However, underwater localization and multi-agent coordination remain challenging due to the lack of Global Navigation Satellite System (GNSS) signals underwater, the high cost of equipping all agents with advanced localization sensors, the limited acoustic communication bandwidth, and the susceptibility of acoustic communication to environmental interference. This work presents a hybrid decentralized framework for robust localization and formation control in teams of AUVs. Two leader agents equipped with GNSS or an advanced Inertial Navigation System (INS) guide a fleet consisting of an arbitrary number of followers in a circular arc formation using acoustic ranging for AUV localization. The framework is validated in a simulated environment and through field trials, demonstrating reliable acoustic localization and formation keeping. The results confirm the viability of the approach for scalable, high-precision underwater monitoring and event response.
Accurately quantifying local sea states is crucial for offshore engineering, but traditional methods face limitations in providing real-time, site-specific updates. The “vessel-as-sensor” paradigm, leveraging existing motion sensors on floating platforms like FPSOs, offers a promising solution. This study presents a machine learning (ML) framework for the inverse estimation of key multi-directional wave parameters (significant wave height $H_{s}$, peak period $T_{p}$, mean wave direction $\bar{\theta}$, spectral enhancement factor $\gamma$, and directional spreading $s$) from rich statistical features of moored FPSO motion-sensor synthetic data. Utilizing time-domain coupled dynamics simulations, 122 statistical features were extracted from 6DOF motion displacements, velocities, and accelerations. The framework compares Artificial Neural Networks (ANNs) and a novel Transformer-based Ensemble (TBE) model. Results demonstrate that rich statistical motion inputs significantly enhance estimation accuracy, particularly for spectral shape parameters ($\gamma$ and $s$), and that TBE consistently outperforms ANNs due to its superior ability to capture nonlinear and interdependent motion features through self-attention mechanisms and ensemble learning. This methodology highlights the potential of floaters as near-real-time wave-sensing devices for various ocean-engineering applications.
Accurate, real-time underwater acoustic sensing is essential for marine biological and ecological studies, compliance monitoring, maritime surveillance, and defense applications. In such applications, densely-populated coherent hydrophone arrays are emerging as important tools for ocean acoustic sensing since they are capable of (1) signal-to-noise ratio (SNR) enhancement, (2) directional sensing and bearing estimation via beamforming, and (3) source spatial localization over instantaneous wide areas. However, capturing high-resolution multichannel acoustic data from deep sea environments presents challenges in power management, bandwidth, pressure tolerance, and data integrity. This paper addresses these challenges by investigating designs and approaches for a compact, low-power, and scalable electronic system capable of reliably digitizing and streaming acoustic data from multiple elements of a coherent hydrophone array over Ethernet in real-time, without relying on bandwidth-limited serial protocols. At the core of the system is a high-performance 24-bit multichannel Analog-to-Digital Converter (ADC) interfaced with a Field-Programmable Gate Array (FPGA). In the future, the system will be designed to be deployable in both air and underwater multi-element acoustic array sensor systems, including coherent hydrophone arrays requiring compact small form factor multichannel ADCs for embedding in oil-filled tubing. Here we demonstrate and validate the design approach by acoustic testing in-air, including waveform inspection and Ethernet frame verification. This platform enables continuous, high-throughput, and low-latency streaming of synchronized multichannel acoustic data from underwater arrays. By eliminating external control and minimizing power consumption, it paves the way for scalable plug-and-play ocean sensing systems designed for deployment in extreme environments.
Sicily faces increasing challenges in managing its water resources due to growing water shortages, driven by climate change, irregular rainfall patterns, and aging or inefficient infrastructure. These issues are particularly critical for the agricultural sector, which is highly dependent on stable and predictable water availability. This study analyzes high-resolution datasets on precipitation and reservoir storage collected over the past two decades. The primary objective is to quantify water losses associated with declining rainfall and reduced reservoir levels, in order to support the evaluation of adaptive strategies, such as infrastructure improvements and the potential integration of desalination technologies.
