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 Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

2021 ◽  
Vol 10 (11) ◽  
pp. 757
Author(s):  
Pin Nie ◽  
Zhenjie Chen ◽  
Nan Xia ◽  
Qiuhao Huang ◽  
Feixue Li
Keyword(s):  
Traffic Management ◽  
Similarity Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Motion Direction ◽  
Maritime Traffic ◽  
A Cell ◽  
Robustness To Noise ◽  
Trajectory Similarity

Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

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.

Research on Vessel Trajectory Multi-Dimensional Compression Algorithm Based on Douglas-Peucker Theory

2014 ◽  
Vol 694 ◽  
pp. 59-62 ◽  
Author(s):  
Fei Xiang Zhu ◽  
Li Ming Miao ◽  
Wen Liu
Keyword(s):  
Compression Algorithm ◽  
Spatial Distance ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Compression Process ◽  
Maritime Traffic ◽  
Offset Distance ◽  
Improved Algorithm

Currently, maritime safety administrations or shipping company had received a large number of vessel trajectory data from Automatic Identification System (AIS). In order to more efficiently carry out research of maritime traffic flow, ship behavior and maritime investigation, it is important to ensure the quality of the vessel trajectory data under compression condition. In classic Douglas-Peucker vector data compression algorithm, offset spatial distance of each point was the single factor in compression process. In order to overcome the shortcomings of classic Douglas-Peucker, a vessel trajectory multi-dimensional compression improved algorithm is proposed. In improved algorithm, the concept of single trajectory point importance which considers the point offset distance and other vessel handling factors, such as the vessel turning angle, speed variation, is proposed to as the compression index. Compared to classic Douglas-Peucker algorithm, experiment results show that the proposed multi-dimensional vessel trajectory compression improved algorithms can effectively retain characteristics of navigation.

AIS Database Powered by GIS Technology for Maritime Safety and Security

2008 ◽  
Vol 61 (4) ◽  
pp. 655-665 ◽  
Author(s):  
Ziqiang Ou ◽  
Jianjun Zhu
Keyword(s):  
Traffic Management ◽  
Surveillance Program ◽  
Automatic Identification ◽  
Identification System ◽  
Gis Technology ◽  
Aerial Surveillance ◽  
Maritime Traffic ◽  
Land Control ◽  
Maritime Domain Awareness ◽  
Fundamental Requirement

The Automatic Identification System (AIS) is an efficient tool to exchange positioning data among participating naval units and land control centres. It was developed primarily as an advanced tool for assistance to sailors during navigation and for the safety of the life at sea. Maritime security has become a major concern for all coastal nations, especially after September 11, 2001. The fundamental requirement is maritime domain awareness via identification, tracking and monitoring of vessels within their waters and this is exactly what an AIS could bring. This paper will be focused on how the AIS-derived information could be used for coastal security, maritime traffic management, vessel tracking and monitoring with the help of GIS technology. The AIS data used in this paper was collected by the Canadian national aerial surveillance program.

Risk of Maritime Traffic in Coastal Waters

2018 ◽  
Author(s):  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Coastal Waters ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Risk Models ◽  
Effective Response ◽  
Maritime Traffic ◽  
Accident Probability ◽  
Probability Prediction ◽  
Organisational Factors

This paper addresses the broad aspects of safety of maritime transportation from the identification to the management of risks related particularly to the maritime traffic in coastal waters. A brief overview of present-day maritime accident statistics are presented and the methodologies that have been adopted in the maritime sector to analyze ship accidents are reviewed. The paper also reviews the models and tools that have been used for simulation of ship navigation and for accident probability prediction based on Automatic Identification System (AIS) data and the analysis and modelling of the influence of human and organisational factors on ship accidents. The development of maritime risk models based on Bayesian Networks and the various elements that influence an effective response to maritime accidents are also addressed.

scholarly journals A Marine Autonomous Surface Craft for Long-Duration, Spatially Explicit, Multidisciplinary Water Column Sampling in Coastal and Estuarine Systems

2015 ◽  
Vol 32 (3) ◽  
pp. 627-641 ◽  
Author(s):  
Daniel L. Codiga
Keyword(s):  
Water Column ◽  
Collision Avoidance ◽  
Rhode Island ◽  
Coast Guard ◽  
Research Quality ◽  
Spatially Explicit ◽  
Automatic Identification ◽  
Identification System ◽  
Current Profile ◽  
Material Transport

AbstractThe Surveying Coastal Ocean Autonomous Profiler (SCOAP) is a large catamaran marine autonomous surface craft (MASC) for unattended weeks-long, spatially explicit, multidisciplinary oceanographic water column profile sampling in coastal/estuarine waterbodies. Material transport rates/pathways, crucial to understanding these ecosystems, are typically poorly known. SCOAP addresses demanding spatiotemporal sampling needs and operational challenges (strong currents, open coastal sea states, complex bathymetry, heavy vessel traffic). Its large size (11-m length, 5-m beam) provides seaworthiness/stability. The average speed of 2.5 m s−1 meets the representative goal to traverse an 18-km transect, sampling 10 min at each of 10 stations 2 km apart, nominally 4 times daily. Efficient hulls and a diesel–electric energy system can provide the needed endurance. The U.S. Coast Guard guidelines are followed: lighting, code flags, the Automatic Identification System (AIS), and collision avoidance regulations (COLREGs)-based collision avoidance (CA) by onboard autonomy software. Large energy reserves obviate low-power optimization of sensors, enabling truly multidisciplinary sampling, and provide on-demand propulsion for effective CA. Vessel stability facilitates high-quality current profile observations and will aid engineering/operation of the planned winched profiling system, performance of an anticipated radar system to detect/track non-AIS vessels, and potential research-quality meteorological sensor operation. A Narragansett Bay test deployment, attended by an escort vessel, met design goals; an unattended open coastal deployment is planned for Rhode Island Sound. Scientific and operational strengths of large catamaran MASCs suggest they could be an important cost-effective complement to other sampling platforms (e.g., improved spatiotemporal coverage and resolution, extending farther inshore, with a broader range of sensors, compared to underwater gliders) in coastal/estuarine waters.

AIS data analytics for adaptive rotating shift in vessel traffic service

2020 ◽  
Vol 120 (4) ◽  
pp. 749-767
Author(s):  
Gangyan Xu ◽  
Chun-Hsien Chen ◽  
Fan Li ◽  
Xuan Qiu
Keyword(s):  
Anomaly Detection ◽  
Data Model ◽  
Data Analytics ◽  
Detection Method ◽  
Traffic Information ◽  
Automatic Identification ◽  
Identification System ◽  
Content Type ◽  
Maritime Traffic ◽  
Free Data

PurposeConsidering the varied and dynamic workload of vessel traffic service (VTS) operators, design an adaptive rotating shift solution to prevent them from getting tired while ensuring continuous high-quality services and finally guarantee a benign maritime traffic environment.Design/methodology/approachThe problem of rotating shift in VTS and its influencing factors are analyzed first, then the framework of automatic identification system (AIS) data analytics is proposed, as well as the data model to extract spatial–temporal information. Besides, K-means-based anomaly detection method is adjusted to generate anomaly-free data, with which the traffic trend analysis and prediction are made. Based on this knowledge, strategies and methods for adaptive rotating shift design are worked out.FindingsIn VTS, vessel number and speed are identified as two most crucial factors influencing operators' workload. Based on the two factors, the proposed data model is verified to be effective on reducing data size and improving data processing efficiency. Besides, the K-means-based anomaly detection method could provide stable results, and the work shift pattern planning algorithm could efficiently generate acceptable solutions based on maritime traffic information.Originality/valueThis is a pioneer work on utilizing maritime traffic data to facilitate the operation management in VTS, which provides a new direction to improve their daily management. Besides, a systematic data-driven solution for adaptive rotating shift is proposed, including knowledge discovery method and decision-making algorithm for adaptive rotating shift design. The technical framework is flexible and can be extended for managing other activities in VTS or adapted in diverse fields.

AIS The Cornerstone of National Security?

2003 ◽  
Vol 56 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Jay A. Creech ◽  
Joseph F. Ryan
Keyword(s):  
National Security ◽  
Coast Guard ◽  
International Maritime Organization ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
The Us ◽  
Pros And Cons ◽  
Technical Issues ◽  
Maritime Domain Awareness

The International Maritime Organization has mandated carriage requirements for VHF Automatic Identification System (AIS) on vessels over 300 tons by 2007 (IMO SOLAS: 1974 and IMO Resolution MSC.99(73)). The AIS will transmit a vessel's position and voyage data to other AIS-equipped vessels and shore-based authorities. It was envisioned that AIS data could enhance the safety of navigation by allowing vessels to quickly identify each other and use Digital Select Calling (DSC) to arrange maneuvers. We will discuss the history and the development of AIS, the technical issues surrounding its use by the mariner as a navigation tool and the pros and cons of the proposal by the US Coast Guard (USCG) to use AIS as a means of surveillance for Maritime Domain Awareness.

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.

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