Marine Search Research Articles

Multi Autonomous Underwater Vehicle (AUV) Distributed Collaborative Search Method Based on a Fuzzy Clustering Map and Policy Iteration

Collaborative search with multiple Autonomous Underwater Vehicles (AUVs) significantly enhances search efficiency compared to the use of a single AUV, facilitating the rapid completion of extensive search tasks. However, challenges arise in underwater environments characterized by weak communication and dynamic complexities. In large marine areas, the limited endurance of a single AUV makes it impossible to cover the entire area, necessitating a collaborative approach using multiple AUVs. Addressing the limited prior information available in uncertain marine environments, this paper proposes a map-construction method using fuzzy clustering based on regional importance. Furthermore, a collaborative search method for large marine areas has been designed using a policy-iteration-based reinforcement learning algorithm. Through continuous sensing and interaction during the marine search process, multiple AUVs constantly update the map of regional importance and iteratively optimize the collaborative search strategy to achieve higher search gains. Simulation results confirm the effective utilization of limited information in uncertain environments and demonstrate enhanced search gains in collaborative scenarios.

Heterogeneous cross domain coordinated control of ASV-AUV system for maritime search and rescue

This paper is concerned with a coordinated search and rescue problem of heterogeneous ocean vehicle cluster composed of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs) subject to model uncertainties and ocean disturbances. Firstly, a three dimensions (3D) cross domain kinematic controller with auto-stop function at the rescue points is designed for the coordinated ASV-AUV system by using a line-of-sight guidance law. Secondly, an event-triggered based communication mechanism is constructed to ensure that the ASV-AUV system can broadcast information at its trigger moments except to the auto-stop points. Finally, an adaptive motion control law based on a low-frequency learning fuzzy predictor is designed, which is able to filter out the high-frequency components of uncertainties and disturbances. The stability of the closed-loop system is proven by employing input-to-state stability and cascade stability analysis. Simulation results and numerical analysis show the effectiveness of the proposed 3D heterogeneous cross domain coordinated path following controller with auto-stop function for marine search and rescue.

On Automatic Person-in-Water Detection for Marine Search and Rescue Operations

On Automatic Person-in-Water Detection for Marine Search and Rescue Operations

An In-Depth Look at China's Aviation Industry's Gender Ratio

Globally, the aviation industry is experiencing an increasing involvement of women, however there are noticeable differences observed across various areas. The investigation of the experiences of Ms. Song Yin, a pilot involved in marine search and rescue operations, highlights the aptitude, perseverance, and determination that women may demonstrate, particularly in challenging and technically complex situations. Furthermore, the prominence of Air India, which proudly exhibits the highest level of female participation in the field of global aviation, serves to emphasize the crucial role that women can and indeed do fulfill within this industry. This study utilizes a qualitative approach to conduct a thorough analysis of the gender dynamics present in China's aviation industry, shedding light on the extent and characteristics of female involvement. The paper argues that the aviation sector in China, despite making improvements, lags behind global equivalents in terms of female participation. In order to address the gender disparity in aviation and fully use the untapped capabilities of women in this field, it is crucial to implement strategic policies and programs that promote a more inclusive and equal environment within the aviation industry.

Real-time monitoring float system applied to offshore engineering

This paper describes a long-range, real-time monitoring and positioning float system based on STM32, which is used to monitor the position information of the equipment in marine engineering. The system adopts a solar power supply to replenish power for the system. It combines the charging control circuit and battery protection module to build a power management module to dynamically adjust the power supply strategy and the system load to realize the long-term operation of the system. Through the LoRa module, the information collected by the pressure sensor, temperature sensor, and GPS positioning module is transmitted to the remote console to realize real-time data collection, sending, and ringing. Finally, the system was tested. The experimental results show that the real-time monitoring float system solves the problem of difficult positioning of equipment and other equipment in marine engineering after falling into the sea. It reduces the difficulty of searching for marine equipment and provides more effective information for marine search and rescue.

Development of the Operational Oceanography Forecasting System in the Northwest Pacific

An operational oceanography forecasting system is established on the base of ROMS and data assimilation method to provide operational predictions and maritime safety applications for the Northwest Pacific. After 10 years spun up through integration, the system reaches a stable field of annual circulation. The model was then integrated from 1990 to 2012 to obtain the assimilation background. After the evaluation of observation data, the ocean forecasting system has been put into operation since 2013 and has been updated for several times. Through the validation of 2018, the system can accurately simulate the main marine environmental characteristics of the northwest pacific. The system can provide high-resolution marine environment fields for red tide, green tide drift and diffusion, oil spill, marine fishery, marine resources development, marine search and rescue, etc.

Cooperative USV–UAV marine search and rescue with visual navigation and reinforcement learning-based control

This paper investigates visual navigation and control of a cooperative unmanned surface vehicle (USV)-unmanned aerial vehicle (UAV) system for marine search and rescue. First, a deep learning-based visual detection architecture is developed to extract positional information from the images taken by the UAV. With specially designed convolutional layers and spatial softmax layers, the visual positioning accuracy and computational efficiency are improved. Next, a reinforcement learning-based USV control strategy is proposed, which could learn a motion control policy with an enhanced ability to reject wave disturbances. The simulation experiment results show that the proposed visual navigation architecture can provide stable and accurate position and heading angle estimation in different weather and lighting conditions. The trained control policy also demonstrates satisfactory USV control ability under wave disturbances.

Target localization using information fusion in WSNs-based Marine search and rescue

Marine search and rescue (MSR) is considered the last line of defense for human life at sea. Recently, a prospective MSR strategy based on wireless sensor networks (WSNs) has been developed, and distress-stricken individuals can be located utilizing various localization methods. Nevertheless, the accuracy cannot satisfy the requirement of related departments, especially when employing a single measurement localization technique, such as received signal strength (RSS)-based technology, in a dynamic and complicated ocean environment. To this end, a scheme inspired by information fusion is developed, which incorporates RSS and time of arrival (TOA) information. The maximum likelihood (ML)-based localization problem is then converted into a hybrid measurement alternative nonnegative constrained least squares (HM-ANCLS) framework. Moreover, the paper develops a two-step linearization localization approach (TLLA) to determine the target location. The first step proposes a slight computation method (SCM) that relies on an active set approach to address the framework. In the second step, the paper presents an error correction approach based on the first-order Taylor series expansion to refine the solution. In addition, the paper conducts the Cramér-Rao low bound (CRLB) and the computational complexity of the hybrid scheme. Simulations reveal that TLLA outperforms other state-of-the-art approaches in various situations.

Determining the drift characteristics of open lifeboats based on large-scale drift experiments

Lifeboat is one of the most important life-saving equipment for escaping at sea when a ship is abandoned in an extreme emergency. An accurate drift model can help rescuers find the drift position of lifeboat in the shortest time, thus improving the efficiency of marine search and rescue (SAR) at sea and ensuring the safety of wrecked people. The purpose of this paper is to investigate the drift characteristics and to develop an accurate drift prediction model for the open lifeboat. First, large-scale drift experiments were conducted to analyze the drift characteristics with three 6.5-meter-long real-size open lifeboats in the South China Sea. Next, three drift prediction models of the lifeboats were developed using the least squares method based on the drift experimental data. Finally, the drift prediction models of the lifeboats were compared and evaluated using the Lagrangian method and Monte Carlo technique, respectively. Results indicate that the probability of positive crosswind leeway (CWL) of the open lifeboat is 47.5%. The jibing frequency is 6% per hour, and the maximum leeway divergence angle is 45°. These drift characteristics are very important for the prediction of the open lifeboat drift trajectory. The comparison results of three drift models show that the improved drift model is more accurate than the other two drift models for predicting drift trajectories of the open lifeboat, which can be directly applied to maritime search and rescue operations in the South China Sea.

Marine Drifting Trajectory Prediction Based on LSTM-DNN Algorithm

In this paper, the long short-term memory with dense neural network (LSTM-DNN) is first introduced to calculate marine drifting trajectory. Based on the Internet of Things technology and the LSTM-DNN algorithm, the marine drifting trajectory model is established. In this model, the information such as wind field, temperature field, ocean current motion field, and target attributes are included, and the influences of the above information on the trajectory model are studied in detail. In order to verify the proposed model, the marine experiments are carried out in the end. The results show that the predicted trajectory data matches well with the experimental trajectory data. By introducing DNN into the algorithm, computational accuracy of drifting trajectory can be significantly improved compared with the conventional LSTM-based prediction model. A detailed comparison of the two algorithms has also been given in the paper. The proposed remote sensing of marine drifting trajectory model can provide a high accurate trajectory prediction and will lead an important guidance in the marine search and rescue work.

Determining the Optimum Location of Ground Control Stations (GCSs) for Unmanned Aerial Vehicles (UAVs) in Marine Search and Rescue (MSAR) Operations

Determining the Optimum Location of Ground Control Stations (GCSs) for Unmanned Aerial Vehicles (UAVs) in Marine Search and Rescue (MSAR) Operations

WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.

Search and Rescue, Climate Change, and the Expansion of the Coast Guard Auxiliary in Inuit Nunangat / the Canadian Arctic

In Canada’s maritime spaces, members of the all-volunteer Canadian Coast Guard Auxiliary (CCGA) provide essential marine search and rescue (SAR) services and promote boating safety. By 2015, however, only nine communities North of 55 possessed Auxiliary units and three of these struggled to remain operational. In 2020, the CCGA counted 20 units in the Coast Guard’s new Arctic Region, with 333 members and 31 vessels—the majority of which are located in Inuit Nunangat (the Inuit homeland in Canada) and comprised of Inuit members—and plans for future expansion.
 Based on stakeholder engagement, government documents, and media analysis, this article assesses the Coast Guard’s Arctic Search and Rescue Project and the concomitant programming under the Oceans Protection Plan that has facilitated the Auxiliary’s expansion in the Arctic. Our analysis asks two overarching questions: Why has this program been able to expand the Auxiliary after previous efforts failed? How has this expansion improved the SAR system and marine safety in Canada’s Arctic, and are there areas for improvement?
 The article makes four primary arguments:
 
 The success of the project has been fueled by strong community engagement and relationship-building efforts, effective data collection that has fostered a better understanding of the marine risks facing Arctic communities, and consistent access to the training and equipment required to safely conduct marine SAR operations
 Members of Arctic Auxiliary units strengthen SAR operations by improving response times, serving as SAR detectives, contributing to marine safety, and, most importantly, by integrating their local and traditional knowledge and skills into the broader search and rescue system.
 Training and organizational gaps exist that should be addressed as the Coast Guard continues to bolster existing units and establish new ones.
 The Arctic SAR Project has provided several best practices and lessons that should guide the implementation of additional resilience-building measures in the North and in other Indigenous communities.

Search and rescue station location selection and conceptual design: a case study of western region of Indonesia

The existence of marine Search and Rescue Units (SRU) to support the search and rescue operations currently are not supported by sufficient infrastructures and facilities. The infrastructures and facilities operated by the National Search and Rescue Agency (NSARA) have not been able to cover all working areas. One example is the location of NSARA dock facilities which is in remote area and has a narrow access channel causes duration to get to the area of accident is quite long. In that condition, the coordination among the SAR vessels and crew cannot perform efficiently and effectively. On the other hand, the National SAR Agency regulation No. 14, the year 2015, describes the necessary infrastructures and facilities that must be provided to support the implementation of marine SAR operations. This paper uses Western Region of Indonesia as a case study aiming to determine the optimum location and conceptual design of SAR station. Set Covering Problem method used to determine SAR station location and SAR vessels required, whereas conceptual design of the infrastructures is established based on data of routes and location selected. From the set covering model, we found 10 locations of SAR station, namely Aceh, South Kalimantan, Bengkulu, South Sumatra Bangka Belitung, Riau, West Kalimantan, Central Java, Riau Islands, and Yogyakarta. There is a total of 20 units of ships deploy with the total operation cost of IDR 263 billion/year (USD 18.5 million/year). The minimum dimensions of the berth have a length of 15 m and a width 5 m, while the gangway has a length of 163 m and a width of 5 m. The total cost of SAR station is IDR 8 billion/year (USD 562,390/year).

An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance

Today’s deep learning architectures, if trained with proper dataset, can be used for object detection in marine search and rescue operations. In this paper a dataset for maritime search and rescue purposes is proposed. It contains aerial-drone videos with 40,000 hand-annotated persons and objects floating in the water, many of small size, which makes them difficult to detect. The second contribution is our proposed object detection method. It is an ensemble composed of a number of the deep convolutional neural networks, orchestrated by the fusion module with the nonlinearly optimized voting weights. The method achieves over 82% of average precision on the new aerial-drone floating objects dataset and outperforms each of the state-of-the-art deep neural networks, such as YOLOv3, -v4, Faster R-CNN, RetinaNet, and SSD300. The dataset is publicly available from the Internet.

RETRACTED: Design of a simulation platform for intelligent marine search and rescue based on wireless sensors

The final line of Maritime Search and Rescue (MSR), maritime security, and marine environmental protection play important roles in the world. Ocean search and wireless sensor-based rescue involve marine objects' activity connected to various components of the ocean and rescue. Given the vastness of the ocean (components and geographical extent), such as actors, multinational corporations, conservation organizations, charities, policies are think tanks, strategies in the arrangement of frameworks, huge national actors themselves have the opportunity. Finding seized is not a surprising rescue. Wireless sensors will eventually expand and note the scale, more advanced research and maritime search and rescue assets. These advances will become a reality to close the opportunity, and many jobs will use the expectations of wireless sensors as expected. Wireless sensors monitor intelligent ocean searches and rescue sent information about it. Many proposed systems suffer from the orthogonal frequency separation performance of response and small when the previous period's length ratio impedes impulse channel. This is the degradation of the Intermediate Carrier Interference (ICI), and Inter-Symbol Interference (ISI) introduced the maximum multipath delay. The reason for this is to reduce the Intermediate Carrier Interference (ICI) itself, an intermediate code through excessive multi-way delay. Optimal Frequency Division Multiplexing (OFDM) beam formation can spatially suppress some multipath using sensing ICI / ISI and take into account the multipath characteristics of explicit channels to reduce interference. Multi-channel faucets provide significant performance improvements for maritime search and avoidance and do not exceed the delay expansion to be resolved.

Airborne hyperspectral detection of underwater camouflaged targets and the effect of target shape and seafloor on image quality

The goal of the present work is to increase the detectivity of hyperspectral imaging systems, deployed on airborne marine search and rescue platforms (SAR), also the increase of the probability for camouflaged target detection and recognition through optimization of image quality. Hence, water column spectral characteristics will be simulated, bringing into consideration the effect of both air layer and plankton layer. This experimental simulation is based on hyperspectral imaging of camouflaged dummy targets of different shapes in a special aquarium designed for this purpose, where an argon laser source was used as multi-spectral line target illuminator and three different bottom backgrounds were tested.

A FAST APPROACH FOR THE LOG-CUMULANTS METHOD APPLIED TO INTENSITY SAR IMAGE PROCESSING

Abstract. Synthetic aperture radar (SAR) image processing and analysis rely on statistical modeling and parameter estimation of the probability density functions that characterize data. The method of log-cumulants (MoLC) is a reliable alternative for parameter estimation of SAR data models and image processing. However, numerical methods are usually applied to estimate parameters using MoLC, and it may lead to a high computational cost. Thus, MoLC may be unsuitable for real-time SAR imagery applications such as change detection and marine search and rescue, for example. Our paper introduces a fast approach to overcome this limitation of MoLC, focusing on parameter estimation of single-channel SAR data modeled by the G0I distribution. Experiments with simulated and real SAR data demonstrate that our approach performs faster than MoLC, while the precision of the estimation is comparable with that of the original MoLC. We tested the fast approach with multitemporal data and applied the arithmetic-geometric distance to real SAR images for change detection on the ocean. The experiments showed that the fast MoLC outperformed the original estimation method with regard to the computational time.

Line of Sight and Image Motion Compensation for Step and Stare Imaging System

In recent years, applications such as marine search and rescue, border patrol, etc. require electro-optical equipment to have both high resolution and precise geographic positioning abilities. The step and stare working based on a composite control system is a preferred solution. This paper proposed a step and stare system composed of two single-axis fast steering mirrors and a two-axis gimbal. The fast steering mirrors (FSMs) realize image motion compensation and the gimbal completes pointing control. The working principle and the working mode of the system are described first. According to the imaging optical path, the algorithm and control flow of the line of sight (LOS) and image motion compensation are developed. The proposed method is verified through ground imaging and flight tests. Under the condition of flight, the pointing accuracy of the target can be controlled within 15 m. The proposed algorithm can achieve effective motion compensation and get high-resolution images. This achieves high resolution and accurate LOS simultaneously.

NMTLAT: A New robust mobile Multi-Target Localization and Tracking Scheme in marine search and rescue wireless sensor networks under Byzantine attack

After a shipwreck (ship collision, explosion, disappearance, etc.), people rely on the marine search and rescue wireless sensor networks (MSR-WSNs) as one key weapon to locate and track drowning targets to save lives and assets. However, due to the complex and dynamic ocean environment, marine sensors could be captured by malicious attackers and then become Byzantine sensors. Byzantine sensors may tamper with the actual sensor data at a fixed or time-varying probability and transmit it to the data fusion center (DFC), which renders the normal localization and tracking function of the search and rescue system downgrade or even fail. To address this issue, we develop a New robust marine mobile Multi-Target LocAlization and Tracking scheme called NMTLAT by eliminating abnormal measurement data from the initial measurement data. NMTLAT works in several steps. By analyzing and mining sensors data and behavior, we firstly employ the information entropy of the system composed of a single sensor and their neighbor sensors to develop an efficient dynamic threshold based Byzantine node identification method. After migrating Byzantine sensors, the DFC utilizes sensor-aware and preprocessed marine data of the beacon or honest sensors to complete target localization and trajectory tracking. Specifically, NMTLAT consists of a novel distributed and cooperative multi-target localization and tracking algorithm using both received signal strength indication (RSSI) and priori information of sensors location. NMTLAT employs the importance sampling method to approximate the posterior probability distribution of the sensors and targets location. Furthermore, when the marine target is outside MSR-WSNs coverage, a piecewise function is utilized to characterize the likelihood of finding a drowning target in the search and rescue sea area. Finally, the Lyapunov’s second stability theorem is adopted to measure the stability of the NMTLAT. Our extensive simulation experiments validate that the NMTLAT performance is superior to existing solutions in various marine search and rescue scenarios.