Ocean Environment Monitoring Research Articles

Research on Autonomous Underwater Vehicle Path Optimization Using a Field Theory-Guided A* Algorithm

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV’s dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments.

An End-to-End Underwater Acoustic Target Recognition Model Based on One-Dimensional Convolution and Transformer

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition.

A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data

High-resolution three-dimensional (3D) variations in ocean temperature and salinity fields are of great significance for ocean environment monitoring. Currently, AI-based 3D temperature and salinity field predictions rely on expensive 3D data, and as the prediction period increases, the stacking of high-resolution 3D data greatly increases the difficulty of model training. This paper transforms the prediction of 3D temperature and salinity into the prediction of sea surface elements and the inversion of subsurface temperature and salinity using sea surface elements, by leveraging the relationship between sea surface factors and subsurface temperature and salinity. This method comprehensively utilizes multi-source ocean data to avoid the issue of data volume caused by stacking high-resolution historical data. Specifically, the model first utilizes 1/4° low-resolution satellite remote sensing data to construct prediction models for sea surface temperature (SST) and sea level anomaly (SLA), and then uses 1/12° high-resolution temperature and salinity data as labels to build an inversion model of subsurface temperature and salinity based on SST and SLA. The prediction model and inversion model are integrated to obtain the final high-resolution 3D temperature and salinity prediction model. Experimental results show that the 20-day prediction results in the two sea areas of the coastal waters of China and the Northwest Pacific show good performance, accurately predicting ocean temperature and salinity in the vast majority of layers, and demonstrate higher resource utilization efficiency.

Ocean wave energy harvesting with high energy density and self-powered monitoring system

Constructing a ocean Internet of Things requires an essential ocean environment monitoring system. However, the widely distributed existing ocean monitoring sensors make it impractical to provide power and transmit monitored information through cables. Therefore, ocean environment monitoring systems particularly need a continuous power supply and wireless transmission capability for monitoring information. Consequently, a high-strength, environmentally multi-compatible, floatable metamaterial energy harvesting device has been designed through integrated dynamic matching optimization of materials, structures, and signal transmission. The self-powered monitoring system breaks through the limitations of cables and batteries in the ultra-low-frequency wave environment (1 to 2 Hz), enabling real-time monitoring of various ocean parameters and wirelessly transmitting the data to the cloud for post-processing. Compared with solar and wind energy in the ocean environment, the energy harvesting device based on the defective state characteristics of metamaterials achieves a high-energy density (99 W/m3). For the first time, a stable power supply for the monitoring system has been realized in various weather conditions (24 h).

Sustainable Sea of Internet of Things: Wind Energy Harvesting System for Unmanned Surface Vehicles.

Harvesting wind energy from the environment and integrating it with the internet of things and artificial intelligence to enable intelligent ocean environment monitoring are effective approach. There are some challenges that limit the performance of wind energy harvesters, such as the larger start-up torque and the narrow operational wind speed range. To address these issues, this paper proposes a wind energy harvesting system with a self-regulation strategy based on piezoelectric and electromagnetic effects to achieve state monitoring for unmanned surface vehicles (USVs). The proposed energy harvesting system comprises eight rotation units with centrifugal adaptation and four piezoelectric units with a magnetic coupling mechanism, which can further reduce the start-up torque and expand the wind speed range. The dynamic model of the energy harvester with the centrifugal effect is explored, and the corresponding structural parameters are analyzed. The simulation and experimental results show that it can obtain a maximum average power of 23.25 mW at a wind speed of 8 m/s. Furthermore, three different magnet configurations are investigated, and the optimal configuration can effectively decrease the resistance torque by 91.25% compared with the traditional mode. A prototype is manufactured, and the test result shows that it can charge a 2200 μF supercapacitor to 6.2 V within 120 s, which indicates that it has a great potential to achieve the self-powered low-power sensors. Finally, a deep learning algorithm is applied to detect the stability of the operation, and the average accuracy reached 95.33%, which validates the feasibility of the state monitoring of USVs.

Seismic noise in South China Sea: High-quality time-frequency analysis from 0.01 to 125 Hz.

An efficient and precise time-frequency analysis method for real-time ocean bottom seismometer (RTOBS) data in the South China Sea (SCS) is presented. Overcoming the limitations of conventional methods, the method involves temporal segmentation, unique frequency octaves, and Fourier transforms to generate power spectral density (PSD) and probability density function profiles. The method demonstrates superior precision, computational efficiency, and full-bandwidth (0 to Nyquist) capability compared to traditional techniques, as validated through theoretical and empirical evaluations. Applied to SCS RTOBS data, it unveils temporal PSD variations, shedding light on underwater noise sources like earthquakes, offshore blasting, ship-induced disturbances, and tidal effects. Establishing background noise levels in the SCS supports noise source categorization and ocean environment monitoring. Furthermore, comparing onshore and offshore seismic stations advances interdisciplinary research, fostering a comprehensive understanding of acoustics and seismology in the region.

Passive Continuous Variable Measurement-Device-Independent Quantum Key Distribution Predictable with Machine Learning in Oceanic Turbulence.

Building an underwater quantum network is necessary for various applications such as ocean exploration, environmental monitoring, and national defense. Motivated by characteristics of the oceanic turbulence channel, we suggest a machine learning approach to predicting the channel characteristics of continuous variable (CV) quantum key distribution (QKD) in challenging seawater environments. We consider the passive continuous variable (CV) measurement-device-independent (MDI) QKD in oceanic scenarios, since the passive-state preparation scheme offers simpler linear elements for preparation, resulting in reduced interaction with the practical environment. To provide a practical reference for underwater quantum communications, we suggest a prediction of transmittance for the ocean quantum links with a given neural network as an example of machine learning algorithms. The results have a good consistency with the real data within the allowable error range; this makes the passive CVQKD more promising for commercialization and implementation.

A new denoising method based on decomposition mixing of hydro-acoustic signal

Hydro-acoustic signal (HAS) contains abundant information. HAS denoising is of great significance for accurate identification of underwater targets, research of military detection equipment and monitoring of ocean ecological environment, and has good civil value and military application value. Because of complex characteristic of ocean circumstance and the temporal change characteristic of hydro-acoustic channel, HAS denoising faces great challenges. To denoise HAS effectively, a new denoising method based on improved variational mode decomposition by jumping spider optimization algorithm (JVMD), weighted fractional order slope entropy (WFrSlEn), dynamic adjacent threshold of correlation coefficient (DATC), improved one dimension (1-D) Gaussian-Laplacian smoothing (IGLS) and adaptive interval correlation wavelet transform (AICWT), named JVMD-WFrSlEn-DATC-IGLS-AICWT, is proposed. JVMD is proposed for the problem that the number of decomposition layer K and penalty factor α cannot be selected adaptively in variational mode decomposition (VMD). WFrSlEn is proposed for the problem that decomposition components are difficult to divide precisely. DATC is proposed for the problem that threshold of correlation coefficient (CC) is selected. IGLS is proposed to preserve more signal details. AICWT is proposed to solve the problem that wavelet transform cannot automatically select appropriate parameters. Firstly, the original signal is decomposed to many intrinsic mode functions (IMFs) with JVMD. Secondly, WFrSlEn and DATC distinguish pure IMFs, mixed IMFs and noisy IMFs. IGLS is performed on mixed IMFs and AICWT is performed on noisy IMFs. Finally, two processed IMFs are reconstructed with pure IMFs to obtain final denoised signal. The results show that the proposed denoising method can improve signal-to-noise ratio by 8∼10 dB for chaotic signal, make phase diagram clearer and smoother, and can effectively suppress noise for HAS.

Incentive Oriented Two-Tier Task Offloading Scheme in Marine Edge Computing Networks: A Hybrid Stackelberg-Auction Game Approach

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

Underwater AUV Navigation Dataset in Natural Scenarios

Autonomous underwater vehicles (AUVs) are extensively utilized in various autonomous underwater missions, encompassing ocean environment monitoring, underwater searching, and geological exploration. Owing to their profound underwater capabilities and robust autonomy, AUVs have emerged as indispensable instruments. Nevertheless, AUVs encounter several constraints in the domain of underwater navigation, primarily stemming from the cost-intensive nature of inertial navigation devices and Doppler velocity logs, which impede the acquisition of navigation data. Underwater simultaneous localization and mapping (SLAM) techniques, along with other navigation approaches reliant on perceptual sensors like vision and sonar, are employed to augment the precision of self-positioning. Particularly within the realm of machine learning, the utilization of extensive datasets for training purposes plays a pivotal role in enhancing algorithmic performance. However, it is common for data obtained exclusively from inertial sensors, a Doppler Velocity Log (DVL), and depth sensors in underwater environments to not be publicly accessible. This research paper introduces an underwater navigation dataset derived from a controllable AUV that is equipped with high-precision fiber-optic inertial sensors, a DVL, and depth sensors. The dataset underwent rigorous testing through numerical calculations and optimization-based algorithms, with the evaluation of various algorithms being based on both the actual surfacing position and the calculated position.

A magnetic coupling wind energy harvester for unmanned surface vehicles

Energy harvesting from the surrounding environment is an effective method to develop a self-powered system and achieve ocean environment monitoring by unmanned surface vehicles. However, a series of challenges, such as narrow wind speed range and low output power, hinder the practical application of wind energy harvesters in real conditions. To tackle these problems, a wind energy harvesting system with double magnets coupling mechanism based on piezoelectric and electromagnetic effect was proposed to enhance wind speed bandwidth and improve output power. The symmetrical opposite magnet arrangement can reduce the start-up torque and boost the adaptability and robustness under the low wind speed environment. In addition, three magnet configurations are explored and corresponding output performances are compared and evaluated. The optimal magnet configuration is obtained in low wind speed conditions. The finite element method analyzes the magnetic coupling mechanism's dynamic performance and electrical properties. The analysis shows that the optimal mode of magnet arrangement can reduce resistance torque by 32.9 % compared with the traditional mode. The experimental results reveal that the proposed double magnets coupling mechanism can broaden wind bandwidth by 1.5 m/s at the low wind speed zone. Additionally, the hybrid energy harvester can obtain maximum average power of 22.2 mW and light up 138 light-emitting diodes. The self-powered application of commercial sensors for unmanned surface vehicles and outdoor tests verified the feasibility and effectiveness of the proposed wind energy harvesting system.

Seawater refractive index sensor based on a cascaded double grating structure

Seawater salinity is a critical physical parameter in oceanography, affecting ocean circulation, marine ecosystems, and climate. However, current salinity measurement methods have limitations in terms of efficiency, convenience, and integration. Therefore, the development of advanced salinity measurement technology is crucial for ocean exploration and research. In this study, we designed a high-sensitivity ocean refractive index sensor using the guided-mode resonance, which exhibits superior performance in terms of transmission efficiency (approximately 1), full width at half maximum (0.210 nm), and figure of merit (5675.924). Furthermore, we obtained a set of optimized parameters by conducting a systematic study on the relationship between different structural parameters and resonance wavelength, which significantly improves the performance. Additionally, the variation trend of resonance wavelength with the change of refractive index in seawater was studied, which demonstrates that our optimized scheme achieves highly sensitive seawater salinity measurement with good process tolerance, polarization insensitivity, and a wide wavelength working interval. This high-sensitivity ocean refractive index sensor has great potential for a wide range of applications in future ocean exploration studies, such as ocean environment monitoring, marine ecology management, and oceanographic research.

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean’s environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean.

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

ObjectiveDuring the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model.MethodologyA hybrid model consists of an upgraded AlexNet with Inception v-4 for salient object detection and ocean environment monitoring. For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). Moreover, we used preprocessing techniques before the classification task for underwater image enhancement, segmentation, noise and fog removal, restoration, and color constancy.ConclusionThe Real-Time Underwater Image Enhancement (RUIE) dataset and the Marine Underwater Environment Database (MUED) dataset are used in this research project’s data fusion and experimental activities, respectively. Root mean square error (RMSE), computing usage, and accuracy are used to construct the model’s simulation results. The suggested model’s relevance form optimization and conspicuous item prediction issues in the seas is illustrated by the greatest accuracy of 95.7% and low RMSE value of 49 when compared to other baseline models.

STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data

Satellite data is of high importance for ocean environment monitoring and protection. However, due to the missing values in satellite data, caused by various force majeure factors such as cloud cover, bad weather and sensor failure, the quality of satellite data is reduced greatly, which hinders the applications of satellite data in practice. Therefore, a variety of methods have been proposed to conduct missing data imputation for satellite data to improve its quality. However, these methods cannot well learn the short-term temporal dependence and dynamic spatial dependence in satellite data, resulting in bad imputation performance when the data missing rate is large. To address this issue, we propose the Spatio-Temporal Attention Generative Adversarial Network (STA-GAN) for missing value imputation in satellite data. First, we develop the Spatio-Temporal Attention (STA) mechanism based on Graph Attention Network (GAT) to learn features for capturing both short-term temporal dependence and dynamic spatial dependence in satellite data. Then, the learned features from STA are fused to enrich the spatio-temporal information for training the generator and discriminator of STA-GAN. Finally, we use the generated imputation data by the trained generator of STA-GAN to fill the missing values in satellite data. Experimental results on real datasets show that STA-GAN largely outperforms the baseline data imputation methods, especially for filling satellite data with large missing rates.

A Practical Method for Blind Pixel Detection for the Push-Broom Thermal-Infrared Hyperspectral Imager.

Thermal infrared hyperspectral imager is one of the frontier payloads in current hyperspectral remote sensing research. It has broad application prospects in land and ocean temperature inversion, environmental monitoring, and other fields. However, due to the influence of the production process of the infrared focal plane array and the characteristics of the material itself, the infrared focal plane array inevitably has blind pixels, resulting in spectral distortion of the data or even invalid data, which limits the application of thermal infrared hyperspectral data. Most of the current blind pixels detection methods are based on the spatial dimension of the image, that is, processing single-band area images. The push-broom thermal infrared hyperspectral imager works completely different from the conventional area array thermal imager, and only one row of data is obtained per scan. Therefore, the current method cannot be directly applied to blind pixels detection of push-broom thermal infrared hyperspectral imagers. Based on the imaging principle of push-broom thermal infrared hyperspectral imager, we propose a practical blind pixels detection method. The method consists of two stages to detect and repair four common types of blind pixels: dead pixel, dark current pixel, blinking pixel, and noise pixel. In the first stage, dead pixels and dark current pixels with a low spectral response rate are detected by spectral filter detection; noise pixels are detected by spatial noise detection; and dark current pixels with a negative response slope are detected by response slope detection. In the second stage, according to the random appearance of blinking pixels, spectral filter detection is used to detect and repair spectral anomalies caused by blinking pixels line by line. In order to verify the effectiveness of the proposed method, a flight test was carried out, using the Airborne Thermal-infrared Hyperspectral Imaging System (ATHIS), the latest thermal infrared imager in China, for data acquisition. The results show that the method proposed in this paper can accurately detect and repair blind pixel, thus effectively eliminating spectral anomalies and significantly improving image quality.

Distributed formation control of underactuated ships with connectivity preservation and collision avoidance

Formation control of ship has distinct advantages in ocean exploration, environment monitoring, patrol surveillance, and maritime rescue with efficiency and flexibility. In this paper, the distributed formation control of underactuated ships, the connectivity preservation, the collision avoidance among formation members, and the collision avoidance with external obstacles are achieved for the first time under the consideration of control input constraints. Taking advantage of nonlinear model predictive control (NMPC), the problem of distributed formation control of underactuated ships with connectivity preservation and collision avoidance is formulated as the quadratic optimization problem with input and output constraints. The periodic event-triggered mechanism is included to solve the optimization problem efficiently. The Floyd method is employed to obtain the optimal communication topology. Multiple studies are conducted to illustrate the advantages of presented methodology.

A Novel Discrete Group Teaching Optimization Algorithm for TSP Path Planning with Unmanned Surface Vehicles

A growing number of researchers are interested in deploying unmanned surface vehicles (USVs) in support of ocean environmental monitoring. To accomplish these missions efficiently, multiple-waypoint path planning strategies for survey USVs are still a key challenge. The multiple-waypoint path planning problem, mathematically equivalent to the traveling salesman problem (TSP), is addressed in this paper using a discrete group teaching optimization algorithm (DGTOA). Generally, the algorithm consists of three phases. In the initialization phase, the DGTOA generates the initial sequence for students through greedy initialization. In the crossover phase, a new greedy crossover algorithm is introduced to increase diversity. In the mutation phase, to balance the exploration and exploitation, this paper proposes a dynamic adaptive neighborhood radius based on triangular probability selection to apply in the shift mutation algorithm, the inversion mutation algorithm, and the 3-opt mutation algorithm. To verify the performance of the DGTOA, fifteen benchmark cases from TSPLIB are implemented to compare the DGTOA with the discrete tree seed algorithm, discrete Jaya algorithm, artificial bee colony optimization, particle swarm optimization-ant colony optimization, and discrete shuffled frog-leaping algorithm. The results demonstrate that the DGTOA is a robust and competitive algorithm, especially for large-scale TSP problems. Meanwhile, the USV simulation results indicate that the DGTOA performs well in terms of exploration and exploitation.

MUST: A Multi-source Spatio-Temporal data fusion Model for short-term sea surface temperature prediction

Sea surface temperature (SST) plays an important role in various oceanic applications, including climate prediction, ocean environment monitoring and marine disaster warning. Although many approaches have been developed for predicting SST, most of them conduct the prediction only based on historical SST data. However, SST is essentially affected by many external factors, e.g., the short-wave radiation from the sun and the long-wave radiation from the atmosphere and ground, which are ignored by existing approaches. In this work, we proposed a Multi-source Spatio-Temporal data fusion model (MUST) to fuse multi-source data, including SST data and external factors, to improve the accuracy of short-term SST prediction. Concretely, MUST first introduces Bicubic Convolutional Interpolation (BCI) to address the issue of inconsistent spatial resolutions of multi-source data, then employs the Spatio-Temporal Dilated ConvLSTM (ST-DC) to learn the spatio-temporal features of SST and external factors, and finally fuses the learned features from multi-source data to predict SST with a Cross Data Fusion (CDF) component. As validated on two real datasets, MUST achieves much better performance than existing SST prediction approaches.

A static-dynamic energy harvester for a self-powered ocean environment monitoring application

A static-dynamic energy harvester for a self-powered ocean environment monitoring application