scholarly journals Fuzzy Functional Dependencies as a Method of Choice for Fusion of AIS and OTHR Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5166 ◽  
Author(s):  
Medhat Abdel Rahman Mohamed Mostafa ◽  
Miljan Vucetic ◽  
Nikola Stojkovic ◽  
Nikola Lekić ◽  
Aleksej Makarov
Keyword(s):  
Situational Awareness ◽  
Data Uncertainty ◽  
Automatic Identification ◽  
Identification System ◽  
Functional Dependencies ◽  
Promising Tool ◽  
Exclusive Economic Zones ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Economic Zones

Maritime situational awareness at over-the-horizon (OTH) distances in exclusive economic zones can be achieved by deploying networks of high-frequency OTH radars (HF-OTHR) in coastal countries along with exploiting automatic identification system (AIS) data. In some regions the reception of AIS messages can be unreliable and with high latency. This leads to difficulties in properly associating AIS data to OTHR tracks. Long history records about the previous whereabouts of vessels based on both OTHR tracks and AIS data can be maintained in order to increase the chances of fusion. If the quantity of data increases significantly, data cleaning can be done in order to minimize system requirements. This process is performed prior to fusing AIS data and observed OTHR tracks. In this paper, we use fuzzy functional dependencies (FFDs) in the context of data fusion from AIS and OTHR sources. The fuzzy logic approach has been shown to be a promising tool for handling data uncertainty from different sensors. The proposed method is experimentally evaluated for fusing AIS data and the target tracks provided by the OTHR installed in the Gulf of Guinea.

Differences in foraging habitat result in contrasting fisheries interactions in two albatross populations

2021 ◽  
Vol 663 ◽  
pp. 197-208
Author(s):  
A Corbeau ◽  
J Collet ◽  
A Pajot ◽  
R Joo ◽  
T Thellier ◽  
...  
Keyword(s):  
Habitat Use ◽  
Habitat Specialization ◽  
Encounter Rate ◽  
Automatic Identification ◽  
Identification System ◽  
Diomedea Exulans ◽  
Exclusive Economic Zones ◽  
Males And Females ◽  
Fisheries Interactions ◽  
Economic Zones

Albatrosses attend fishing boats to feed on fishing discards but are often at risk of accidental bycatch. To examine whether populations (same species) and sexes differ in their overlap with fisheries due to differences in habitat use, we combined the use of recently developed loggers equipped with GPS and boat radar detectors with Automatic Identification System (AIS) data. Our study indicates that incubating wandering albatrosses Diomedea exulans from Crozet and Kerguelen foraged in different habitats although the duration of trips was similar. Both female and male Kerguelen birds took advantage of the large and productive surrounding shelf, whereas Crozet birds used the small shelf around the islands to a lesser extent. In Crozet, there was segregation between males and females, the latter favouring deeper and warmer waters. The 2 strategies of habitat use led to different overlap and attraction to boats, with Kerguelen birds encountering and attending boats for longer and at closer proximity to the colony than Crozet birds. Crozet females encountered boats at greater distances from the colony than males. Because of their different habitat use and foraging outside exclusive economic zones (EEZ) and further from the colony, Crozet birds attended more non-declared boats (without AIS) than Kerguelen birds. Albatrosses were more attracted by fisheries than cargo vessels and were especially attracted by fishing discards that led them to attend vessels for longer periods for both sexes and populations. The differences found between populations and individuals in terms of habitat specialization and encounter rate of fisheries should be considered for future assessments of risk of bycatch.

scholarly journals Ocean sentinel albatrosses locate illegal vessels and provide the first estimate of the extent of nondeclared fishing

2020 ◽  
Vol 117 (6) ◽  
pp. 3006-3014 ◽  
Author(s):  
Henri Weimerskirch ◽  
Julien Collet ◽  
Alexandre Corbeau ◽  
Adrien Pajot ◽  
Floran Hoarau ◽  
...  
Keyword(s):  
Threatened Species ◽  
Automatic Identification ◽  
Identification System ◽  
Fish Stocks ◽  
Conservation Policies ◽  
Fishing Vessels ◽  
Exclusive Economic Zones ◽  
Assessment Plans ◽  
Crucial Information ◽  
Economic Zones

With threats to nature becoming increasingly prominent, in order for biodiversity levels to persist, there is a critical need to improve implementation of conservation measures. In the oceans, the surveillance of fisheries is complex and inadequate, such that quantifying and locating nondeclared and illegal fisheries is persistently problematic. Given that these activities dramatically impact oceanic ecosystems, through overexploitation of fish stocks and bycatch of threatened species, innovative ways to monitor the oceans are urgently required. Here, we describe a concept of “Ocean Sentinel” using animals equipped with state-of-the-art loggers which monitor fisheries in remote areas. Albatrosses fitted with loggers detecting and locating the presence of vessels and transmitting the information immediately to authorities allowed an estimation of the proportion of nondeclared fishing vessels operating in national and international waters of the Southern Ocean. We found that in international waters, more than one-third of vessels had no Automatic Identification System operating; in national Exclusive Economic Zones (EEZs), this proportion was lower on average, but variable according to EEZ. Ocean Sentinel was also able to provide unpreceded information on the attraction of seabirds to vessels, giving access to crucial information for risk-assessment plans of threatened species. Attraction differed between species, age, and vessel activity. Fishing vessels attracted more birds than other vessels, and juveniles both encountered fewer vessels and showed a lower attraction to vessels than adults. This study shows that the development of technologies offers the potential of implementing conservation policies by using wide-ranging seabirds to patrol oceans.

scholarly journals An unmanned aircraft system for maritime operations

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878633 ◽  
Author(s):  
Mario Monteiro Marques ◽  
Victor Lobo ◽  
R Batista ◽  
J Oliveira ◽  
A Pedro Aguiar ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Situational Awareness ◽  
Traffic Monitoring ◽  
Unmanned Aircraft ◽  
Automatic Identification ◽  
Identification System ◽  
Time Operation ◽  
Air System ◽  
Unmanned Air System ◽  
Aircraft System

Unmanned air systems are becoming ever more important in modern societies but raise a number of unresolved problems. There are legal issues with the operation of these vehicles in nonsegregated airspace, and a pressing requirement to solve these issues is the development and testing of reliable and safe mechanisms to avoid collision in flight. In this article, we describe a sense and avoid subsystem developed for a maritime patrol unmanned air system. The article starts with a description of the unmanned air system, that was developed specifically for maritime patrol operations, and proceeds with a discussion of possible ways to guarantee that the unmanned air system does not collide with other flying objects. In the system developed, the position of the unmanned air system is obtained by the global positioning system and that of other flying objects is reported via a data link with a ground control station. This assumes that the detection of those flying objects is done by a radar in the ground or by self-reporting via a traffic monitoring system (such as automatic identification system). The algorithm developed is based on game theory. The approach is to handle both the procedures, threat detection phase and collision avoidance maneuver, in a unified fashion, where the optimal command for each possible relative attitude of the obstacle is computed off-line, therefore requiring low processing power for real-time operation. This work was done under the research project named SEAGULL that aims to improve maritime situational awareness using fleets of unmanned air system, where collision avoidance becomes a major concern.

Securing the Automatic Identification System (AIS): Using public key cryptography to prevent spoofing whilst retaining backwards compatibility

2021 ◽  
pp. 1-13
Author(s):  
Gareth Wimpenny ◽  
Jan Šafář ◽  
Alan Grant ◽  
Martin Bransby
Keyword(s):  
Data Exchange ◽  
Situational Awareness ◽  
Public Key Cryptography ◽  
Satellite System ◽  
Public Key ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Safe Navigation ◽  
Global Navigation Satellite

Abstract The civilian Automatic Identification System (AIS) has no inherent protection against spoofing. Spoofed AIS messages have the potential to interfere with the safe navigation of a vessel by, amongst other approaches, spoofing maritime virtual aids to navigation and/or differential global navigation satellite system (DGNSS) correction data conveyed across it. Acting maliciously, a single transmitter may spoof thousands of AIS messages per minute with the potential to cause considerable nuisance; compromising information provided by AIS intended to enhance the mariner's situational awareness. This work describes an approach to authenticate AIS messages using public key cryptography (PKC) and thus provide unequivocal evidence that AIS messages originate from genuine sources and so can be trusted. Improvements to the proposed AIS authentication scheme are identified which address a security weakness and help avoid false positives to spoofing caused by changes to message syntax. A channel loading investigation concludes that sufficient bandwidth is available to routinely authenticate all AIS messages whilst retaining backwards compatibility by carrying PKC ‘digital signatures’ in a separate VHF Data Exchange System (VDES) side channel.

Inland Ship Trajectory Restoration by Recurrent Neural Network

2019 ◽  
Vol 72 (06) ◽  
pp. 1359-1377 ◽  
Author(s):  
Cheng Zhong ◽  
Zhonglian Jiang ◽  
Xiumin Chu ◽  
Lei Liu
Keyword(s):  
Neural Networks ◽  
Situational Awareness ◽  
Short Term Memory ◽  
Linear Method ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Proposed Model ◽  
Data Restoration ◽  
Complicated Geometry

The quality of Automatic Identification System (AIS) data is of fundamental importance for maritime situational awareness and navigation risk assessment. To improve operational efficiency, a deep learning method based on Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) is proposed and applied in AIS trajectory data restoration. Case studies have been conducted in two distinct reaches of the Yangtze River and the capability of the proposed method has been evaluated. Comparisons have been made between the BLSTM-RNNs-based method and the linear method and classic Artificial Neural Networks. Satisfactory results have been obtained by all methods in straight waterways while the BLSTM-RNNs-based method is superior in meandering waterways. Owing to the bi-directional prediction nature of the proposed method, ship trajectory restoration is favourable for complicated geometry and multiple missing points cases. The residual error of the proposed model is computed through Euclidean distance which decreases to an order of 10 m. It is considered that the present study could provide an alternative method for improving AIS data quality, thus ensuring its completeness and reliability.

Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering

2017 ◽  
Vol 70 (6) ◽  
pp. 1383-1400 ◽  
Author(s):  
Jiang Wang ◽  
Cheng Zhu ◽  
Yun Zhou ◽  
Weiming Zhang
Keyword(s):  
Knowledge Discovery ◽  
Situational Awareness ◽  
Automatic Identification ◽  
Identification System ◽  
Mobility Patterns ◽  
Multidimensional Arrays ◽  
Temporal Knowledge Discovery ◽  
Spatio Temporal ◽  
Vessel Motion ◽  
Occurrence Patterns

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

scholarly journals Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches

2022 ◽  
Vol 10 (1) ◽  
pp. 112
Author(s):  
Konrad Wolsing ◽  
Linus Roepert ◽  
Jan Bauer ◽  
Klaus Wehrle
Keyword(s):  
Risk Assessment ◽  
Anomaly Detection ◽  
Situational Awareness ◽  
Automatic Detection ◽  
Use Cases ◽  
Research Articles ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Potential Use

The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.

MSARI: A Database for Large Volume Storage and Utilisation of Maritime Data

2016 ◽  
Vol 70 (2) ◽  
pp. 276-290 ◽  
Author(s):  
Anthony W. Isenor ◽  
Marie-Odette St-Hilaire ◽  
Sean Webb ◽  
Michel Mayrand
Keyword(s):  
Situational Awareness ◽  
Automatic Identification ◽  
Identification System ◽  
Product Management ◽  
Long Term Storage ◽  
Maritime Traffic ◽  
Data Volume ◽  
Maritime Domain Awareness ◽  
Meet Data

The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefits to the maritime domain awareness research community by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilising open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as that available from AIS.

scholarly journals AIS Data Vulnerability Indicated by a Spoofing Case-Study

2021 ◽  
Vol 11 (11) ◽  
pp. 5015
Author(s):  
Andrej Androjna ◽  
Marko Perkovič ◽  
Ivica Pavic ◽  
Jakša Mišković
Keyword(s):  
Situational Awareness ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Position Information ◽  
Safe Navigation ◽  
The Face ◽  
The Impact ◽  
Source Of Information

This paper takes a close look at the landscape of the Automatic Identification System (AIS) as a major source of information for maritime situational awareness (MSA) and identifies its vulnerabilities and challenges for safe navigation and shipping. As an important subset of cyber threats affecting many maritime systems, the AIS is subject to problems of tampering and reliability; indeed, the messages received may be inadvertently false, jammed, or intentionally spoofed. A systematic literature review was conducted for this article, complemented by a case study of a specific spoofing event near Elba in December 2019, which confirmed that the typical maritime AIS could be easily spoofed and generate erroneous position information. This intentional spoofing has affected navigation in international waters and passage through territorial waters. The maritime industry is neither immune to cyberattacks nor fully prepared for the risks associated with the use of modern digital systems. Maintaining seaworthiness in the face of the impact of digital technologies requires a robust cybersecurity framework.

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