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 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.

A Statistical Model for Vessel-to-Vessel Distances to Evaluate Radar Interference

2017 ◽  
Vol 70 (5) ◽  
pp. 1098-1116 ◽  
Author(s):  
Gaspare Galati ◽  
Gabriele Pavan ◽  
Francesco De Palo ◽  
Giuseppe Ragonesi
Keyword(s):  
Solid State ◽  
Density Function ◽  
Coast Guard ◽  
Automatic Identification ◽  
Identification System ◽  
Limited Area ◽  
Maritime Traffic ◽  
The Mean ◽  
New Generation ◽  
Italian Coast

Maritime traffic has significantly increased in recent decades due to its advantageous costs, delivery rate and environmental compatibility. With the advent of the new generation of marine radars, based on the solid-state transmitter technology that calls for much longer transmitted pulses, the interference problem can become critical. Knowing the positions and the heights of the ships, the mean number of the vessels in radar range can be estimated to evaluate the effects of their mutual radar interferences. This paper aims to estimate the probability density function of the mutual distances. The truncation of the density function within a limited area related to horizon visibility leads to a simple single-parameter expression, useful to classify the ships as either randomly distributed or following a defined route. Practical results have been obtained using Automatic Identification System (AIS) data provided by the Italian Coast Guard in the Mediterranean Sea.

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.

Identification System for Moving Objects Based on Parallel Shift Technology

2020 ◽  
pp. 374-387
Author(s):  
Sergey Yuzhakov ◽  
Stepan Mykolayovych Bilan
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Ordered Set ◽  
Automatic Identification ◽  
Identification System ◽  
Automated Identification ◽  
New Methods ◽  
Parallel Shift

There are tasks of automatic identification of the moving stock of the railway, one of which is the automatic identification of rail cars cars by their number plates. Different organizational, legal, moral and ethical, technical, and programmatic methods of automated identification are used to solve this problem. At present little attention is paid to the development of means of automatic identification of moving objects, which would be possible regardless of the orientation and shape of the figure, especially if it concerns the recognition of freely oriented images of number plates. Therefore, many new methods for recognizing of number plates are developing. In the chapter, the system of identification of objects by their number plates in real time is considered. On moving objects (moving stock of a railway), an identifier image is drawn, which is an ordered set of characters. As a rule, these are numbers. But there may be other characters. The work also discusses the method of identification images of number plates with a high percentage of noise.

Mobile GIS and Real-Time Data Display in a Common Operating Picture

2014 ◽  
Vol 2014 (1) ◽  
pp. 1607-1620
Author(s):  
Andrew Milanes ◽  
Mark Stevens ◽  
David Alford
Keyword(s):  
Real Time ◽  
Streaming Data ◽  
Live Streaming ◽  
Automatic Identification ◽  
Identification System ◽  
Time Data ◽  
Incident Command ◽  
Gis Data ◽  
Multiple Data Sets ◽  
Common Operating Picture

ABSTRACT Geographic Information Systems (GIS) has become an integral component to the data management, analysis, and presentation needs during an emergency response. GIS allows for the rapid integration of multiple data sets and is a tool utilized throughout the Incident Command System to aid in timely, informed decision making. Advances in mobile and hand-held devices, such as smart-phones, tablets and GPSs have provided new capabilities in field GIS data collection and dissemination. In addition to GIS data, live streaming data feeds, such as vessel Automatic Identification System (AIS) and video from remotely operated vehicles (ROVs), have become increasingly important to situational awareness. Prompt broadcasting of this data in a Common Operating Picture (COP) framework has become critical as the demand for real-time incident information increases.

scholarly journals Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 871
Author(s):  
Yongpeng Wang ◽  
Daisuke Watanabe ◽  
Enna Hirata ◽  
Shigeki Toriumi
Keyword(s):  
Carbon Dioxide ◽  
Real Time ◽  
Time Management ◽  
Carbon Dioxide Emissions ◽  
Fixed Time ◽  
Automatic Identification ◽  
Identification System ◽  
Time Interval ◽  
Trajectory Data ◽  
Fixed Time Interval

In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way.

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.

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