scholarly journals Trajectory Data Restoring: A Way of Visual Analysis of Vessel Identity Base on OPTICS

2021 ◽  
Author(s):  
Jinyu Lei ◽  
Xiumin Chu ◽  
Wei He
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Situational Awareness ◽  
Empirical Test ◽  
Automatic Identification ◽  
Identification System ◽  
Mobile Service ◽  
Trajectory Data ◽  
Marine Traffic ◽  
Time Distance

Automatic identification system (AIS) data is a significant analysis and decision-making basis for maritime situational awareness. Because of particular navigation environment and the vulnerability of AIS equipment onboard, results in the phenomenon that numerous vessels share the same Maritime Mobile Service Identity (MMSI) in the AIS data collected in ocean and inland waterway. This kind of mixed trajectory information dramatically affects the judgement of the maritime manager and supervisors. In this paper, the visual analytics combined with the algorithm named Ordering Points to Identify the Clustering Structure (OPTICS) is adopted to realize the separation of vessels sharing same MMSI, which can help analysts to recognize the vessel trajectory information and assess the risk of marine traffic correctly. Firstly, this paper illustrates the application of OPTICS clustering method based on space-time distance in AIS trajectory separation. Secondly, the display and interaction of trajectory information of Vessels sharing the same MMSI in OpenStreetMap map were introduced. Then visual analysis method is applied to optimize the parameters of the algorithm and display the trajectory separation effect corresponding to different settings. In final, various practical situations are discussed, and the empirical test shows that it is feasible in AIS chaos trajectory separation.

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.

scholarly journals A Visual Analysis Approach to Understand and Explore Quality Problems of AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 198
Author(s):  
Wei He ◽  
Jinyu Lei ◽  
Xiumin Chu ◽  
Shuo Xie ◽  
Cheng Zhong ◽  
...  
Keyword(s):  
Data Quality ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Visual Exploration ◽  
Quality Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Interaction Method ◽  
Related Quality ◽  
Ship Behavior

Low quality automatic identification system (AIS) data often mislead analysts to a misunderstanding of ship behavior analysis and to making incorrect navigation risk assessments. It is therefore necessary to accurately understand and judge the quality problems in AIS data before a further analysis of ship behavior. Outliers were filtered in the existing methods of AIS quality analysis based only on mathematical models where AIS data related quality problems are not utilized and there is a lack of visual exploration. Thus, the human brain’s ability cannot be fully utilized to think visually and for reasoning. In this regard, a visual analytics (VA) approach called AIS Data Quality visualization (ADQvis) was designed and implemented here to support evaluations and explorations of AIS data quality. The system interface is overviewed and then the visualization model and corresponding human-computer interaction method are described in detail. Finally, case studies were carried out to demonstrate the effectiveness of our visual analytics approach for AIS quality problems.

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

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.

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.

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.

scholarly journals Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data

2020 ◽  
Vol 10 (11) ◽  
pp. 4010 ◽  
Author(s):  
Kwang-il Kim ◽  
Keon Myung Lee
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Gear ◽  
Trajectory Data ◽  
Data Set ◽  
Prediction Module ◽  
Fishing Gears ◽  
Fully Connected

Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

scholarly journals Evaluating Strong Currents at a Fairway in the Finnish Archipelago Sea

2018 ◽  
Vol 6 (4) ◽  
pp. 122 ◽  
Author(s):  
Hedi Kanarik ◽  
Laura Tuomi ◽  
Pekka Alenius ◽  
Mikko Lensu ◽  
Elina Miettunen ◽  
...  
Keyword(s):  
Cross Section ◽  
Acoustic Doppler Current Profiler ◽  
Mean Value ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Traffic ◽  
Current Profiler ◽  
System Data ◽  
The Cross ◽  
Oceanographic Conditions

Safe navigation in complex archipelagos requires knowledge and understanding of oceanographic conditions in the fairways. We have studied oceanographic conditions and their relation to weather in a crossing in the Finnish archipelago, which is known to have events when strong currents affect marine traffic. Our main dataset is ADCP (Acoustic Doppler Current Profiler) current measurements, done in the cross section of five months in 2013. We found that the local currents flow mainly to two directions, either to north-northeast (NNE) or to south-southwest (SSW), which is nearly perpendicular to the deepest fairway in the area. The mean value of the currents in the surface layer was 0.087 ms - 1 , but during the high wind situations, the current speed rose over 0.4 ms - 1 . These strong currents were also shown, according to AIS (Automatic Identification System) data, to cause drift of the vessels passing the cross section, though the effect of wind and current to the ship may sometimes be hard to separate. We studied whether the strong currents could be predicted from routine observations of wind and sea level available in the area, and we found that prediction of these currents is possible to some extent. We also found that winds of over 10 ms - 1 blowing from NW (300 ∘ –350 ∘ ) and SE (135 ∘ –180 ∘ ) generated strong currents of over 0.2 ms - 1 , whereas most commonly measured winds from SW (190 ∘ –275 ∘ ) did not generate currents even with winds as high as 15 ms - 1 .

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.

scholarly journals A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area

2009 ◽  
Vol 9 (4) ◽  
pp. 15339-15373 ◽  
Author(s):  
J.-P. Jalkanen ◽  
A. Brink ◽  
J. Kalli ◽  
H. Pettersson ◽  
J. Kukkonen ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Fuel Consumption ◽  
Exhaust Emissions ◽  
Automatic Identification ◽  
Identification System ◽  
The Baltic Sea ◽  
Support Tool ◽  
Marine Traffic ◽  
The Baltic ◽  
Modelling System

Abstract. A method is presented for the evaluation of the exhaust emissions of marine traffic, based on the messages provided by the Automatic Identification System (AIS), which enable the identification and location determination of ships. The use of the AIS data enables the positioning of ship emissions with a high spatial resolution, which is limited only by the inaccuracies of the Global Positioning System (typically a few metres) that is used in vessel navigation. The emissions are computed based on the relationship of the instantaneous speed to the design speed, and these computations also take into account the detailed technical information of the ships' engines. The modelling of emissions is also based on a few basic equations of ship design, including the modelling of the propelling power of each vessel in terms of its speed. We have also investigated the effect of waves on the consumption of fuel, and on the emissions to the atmosphere. The predictions of fuel consumption were compared with the actual values obtained from the shipowners. For a RoPax vessel, the predicted and reported values of fuel consumption agreed within an accuracy of 6%. According to the data analysis and model computations, the emissions of NOx, SOx and CO2 originating from ships in the Baltic Sea in 2007 were in total 400 kt, 138 kt and 19 Mt, respectively. A breakdown of emissions by flag state, ship's type and year of construction is also presented. The modelling system can be used as a decision support tool in the case of issues concerning, e.g., health effects caused by shipping emissions, the construction of emission-based fairway dues systems or emissions trading. The computation of emissions can also be automated, which will save resources in constructing emission inventories. Both the methodologies and the emission computation program can be applied in any sea region in the world, provided that the AIS data from that specific region are available.

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