scholarly journals Near Real-time S-AIS

Pomorstvo ◽  
2018 ◽  
Vol 31 (1) ◽  
pp. 211-218 ◽  
Author(s):  
Davor Šakan ◽  
Igor Rudan ◽  
Srđan Žuškin ◽  
David Brčić
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Service Providers ◽  
Traffic Monitoring ◽  
Environmental Research ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Recent Developments ◽  
Processing Techniques

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

scholarly journals Near Real-time S-AIS

Pomorstvo ◽  
2018 ◽  
Vol 31 (2) ◽  
pp. 211-218
Author(s):  
Davor Šakan ◽  
Igor Rudan ◽  
Srđan Žuškin ◽  
David Brčić
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Service Providers ◽  
Traffic Monitoring ◽  
Environmental Research ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Recent Developments ◽  
Processing Techniques

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

scholarly journals Near Real-time S-AIS

Pomorstvo ◽  
2018 ◽  
Vol 32 (2) ◽  
pp. 211-218
Author(s):  
Davor Šakan ◽  
Igor Rudan ◽  
Srđan Žuškin ◽  
David Brčić
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Service Providers ◽  
Traffic Monitoring ◽  
Environmental Research ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Recent Developments ◽  
Processing Techniques

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

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.

Data Reception Analysis of the AIS on board the TianTuo-3 Satellite

2017 ◽  
Vol 70 (4) ◽  
pp. 761-774 ◽  
Author(s):  
Shiyou Li ◽  
Xiaoqian Chen ◽  
Lihu Chen ◽  
Yong Zhao ◽  
Tao Sheng ◽  
...  
Keyword(s):  
Detailed Analysis ◽  
Real Time ◽  
Long Range ◽  
Automatic Identification ◽  
Identification System ◽  
Detection Ability ◽  
Class A ◽  
Frequency Detection ◽  
Real Time Tracking ◽  
New Generation

The Automatic Identification System (AIS) receiver on board the main satellite of the TianTuo-3 constellation, LvLiang-1, is a new generation of AIS receiver. Having partly solved the signal conflict problems and with larger coverage over the ground, the AIS receiver on board TianTuo-3 greatly improves the signal detection ability. The data received by the AIS receiver during the TianTuo-3 debugging stage is employed for detailed analysis in this paper. Results include: TianTuo-3 implements four-frequency detection at the same time, and a time-flag is inserted into the received AIS data, a small portion of Class A vessels (at least 1480) have been equipped with AIS sending the long range AIS broadcast message with two new frequency channels and the hourly averaged count of the message received by TianTuo-3’s AIS is between 1500 ~ 2500. This AIS receiver is capable of real-time tracking a single vessel. In conclusion, the TianTuo-3 space-based AIS receiver is capable of continuously receiving AIS messages sent by global maritime vessels.

scholarly journals MARITIME BIG DATA ANALYSIS WITH ARLAS

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 71-76
Author(s):  
W. Gautier ◽  
S. Falquier ◽  
S. Gaudan
Keyword(s):  
Big Data ◽  
Open Source ◽  
Big Data Analysis ◽  
Machine Learning Algorithms ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Huge Amount ◽  
Processing Platform

Abstract. The maritime industry has become a major part of globalization. Political and economic actors are meeting challenges regarding shipping and people transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels and delivers a huge amount of information that can be used to analyze fluxes and behaviors. However, the exploitation of these numerous messages requires tools based on Big Data principles.Acknowledgement of origin, destination, travel duration and distance of each vessel can help transporters to manage their fleet and ports to analyze fluxes and focus their investigations on some containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, an open source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create “courses”, embodying the travel of the vessel. Then we derive the travel indicators. The visualization of results is made possible by ARLAS Exploration, an open source and scalable tool to explore geolocated data. This carto-centered application allows users to navigate into the huge amount of enriched data and helps to take benefits of these new origin and destination indicators. This tool can also be used to help in the creation of Machine Learning algorithms in order to deal with many maritime transportation challenges.

scholarly journals Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

2015 ◽  
Vol 12 (4) ◽  
pp. 1327-1388 ◽  
Author(s):  
R. Fernandes ◽  
F. Braunschweig ◽  
F. Lourenço ◽  
R. Neves
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Oil Spill ◽  
Situational Awareness ◽  
Assessment Tool ◽  
Weather Conditions ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Decision Support Model ◽  
Risk Levels

Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable shoreline risk levels from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – Portuguese Continental shelf – with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time. Shoreline risks can be computed in real-time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns, "hot spots" or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

scholarly journals A Novel Framework of Real-Time Regional Collision Risk Prediction Based on the RNN Approach

2020 ◽  
Vol 8 (3) ◽  
pp. 224 ◽  
Author(s):  
Dapei Liu ◽  
Xin Wang ◽  
Yao Cai ◽  
Zihao Liu ◽  
Zheng-Jiang Liu
Keyword(s):  
Real Time ◽  
Shapley Value ◽  
Spatial Clustering ◽  
Risk Identification ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Short Time ◽  
Sea Area

Regional collision risk identification and prediction is important for traffic surveillance in maritime transportation. This study proposes a framework of real-time prediction for regional collision risk by combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique, Shapley value method and Recurrent Neural Network (RNN). Firstly, the DBSCAN technique is applied to cluster vessels in specific sea area. Then the regional collision risk is quantified by calculating the contribution of each vessel and each cluster with Shapley value method. Afterwards, the optimized RNN method is employed to predict the regional collision risk of specific seas in short time. As a result, the framework is able to determine and forecast the regional collision risk precisely. At last, a case study is carried out with actual Automatic Identification System (AIS) data, the results show that the proposed framework is an effective tool for regional collision risk identification and prediction.

scholarly journals Methods for Processing and Interpretation of AIS Signals Corrupted by Noise and Packet Collisions

2012 ◽  
Vol 49 (3) ◽  
pp. 25-31 ◽  
Author(s):  
J. Poļevskis ◽  
M. Krastiņš ◽  
G. Korāts ◽  
A. Skorodumovs ◽  
J. Trokšs
Keyword(s):  
Software Defined Radio ◽  
Traffic Monitoring ◽  
Automatic Identification ◽  
Identification System ◽  
Coverage Area ◽  
Amplitude Noise ◽  
Packet Collision ◽  
Marine Traffic ◽  
The Common

Methods for Processing and Interpretation of AIS Signals Corrupted by Noise and Packet Collisions The authors deal with the operation of Automatic Identification System (AIS) used in the marine traffic monitoring to broadcast messages containing information about the vessel: id, payload, size, speed, destination etc., meant primarily for avoidance of ship collisions. To extend the radius of AIS operation, it is envisaged to dispose its receivers on satellites. However, in space, due to a large coverage area, interfering factors are especially pronounced - such as packet collision, Doppler's shift and noise impact on AIS message receiving, pre-processing and decoding. To assess the quality of an AIS receiver's operation, a test was carried out in which, varying automatically frequency, amplitude, noise, and other parameters, the data on the ability of the receiver's ability to decode AIS signals are collected. In the work, both hardware- and software-based AIS decoders were tested. As a result, quite satisfactory statistics has been gathered - both on the common and the differing features of such decoders when operating in space. To obtain reliable data on the software-defined radio AIS receivers, further research is envisaged.

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