Simulating Surveillance Options for the Canadian North

2016 ◽  
Vol 69 (5) ◽  
pp. 940-954 ◽  
Author(s):  
Anna-Liesa S. Lapinski ◽  
Anthony W. Isenor ◽  
Sean Webb
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Sufficient Information ◽  
End User ◽  
Additional Information ◽  
Canadian North ◽  
Traffic Awareness ◽  
Simulation Scenario ◽  
Research Goal ◽  
Wide Area Surveillance

As part of the overarching research goal to assess current and potential maritime information sources for use in maritime defence and security in the Canadian north, we examine whether wide-area surveillance data, as represented by Space-based Automatic Identification System (S-AIS) data, offers sufficient information for surveillance requirements in the Canadian north. If S-AIS data are not sufficient, we address how the additional information provided by Long-Range Identification and Tracking (LRIT) can be used to meet the surveillance requirements. A Systems Tool Kit (STK) simulation scenario is constructed that includes five exactEarth satellites collecting AIS data. Simulated AIS transmitters are positioned at 20 northern Canada ground locations. The results indicate that for each location, two thirds of the eight-day simulation is spent without a satellite within range, when using the five satellites. As the number of satellites decreases, intervals in the range of 80 to 105 minutes, during which there are no AIS messages received, increase in frequency. If the end-user requires vessel location information more often than S-AIS consistently provides, augmenting the S-AIS information with LRIT polling should achieve the desired vessel traffic awareness.

scholarly journals Using Automatic Identification System (AIS) Data to Estimate Whale Watching Effort

2021 ◽  
Vol 8 ◽  
Author(s):  
Javier Almunia ◽  
Patricia Delponti ◽  
Fernando Rosa
Keyword(s):  
Information Technologies ◽  
Spatial Information ◽  
Sustainable Use ◽  
Automatic Identification ◽  
Identification System ◽  
Sufficient Information ◽  
Whale Watching ◽  
Negative Effects ◽  
Automatic Identification System ◽  
Maritime Navigation

The growing concerns about the negative effects caused by whale watching on wild cetacean populations are evincing the need to measure whale watching effort more precisely. The current alternatives do not provide sufficient information or imply time-consuming and staff-intensive tasks that limit their effectiveness to establish the maximum carrying capacity for this tourist activity. A methodology based on big data analysis, using Automatic Identification System (AIS) messages can provide valuable vessel activity information, which is necessary to estimate whale watching effort in areas with cetacean populations. We used AIS data to automatically detect whale watching operations and quantify whale watching effort with high spatial and temporal resolution in the Canary Islands off the west African coast. The results obtained in this study are very encouraging, proving that the methodology can estimate seasonal and annual trends in the whale watching effort. The methodology has also proved to be effective in providing detailed spatial information about the whale watching effort, which makes an interesting tool to manage spatial regulations and enforce exclusion zones. The widespread use of AIS devices in maritime navigation provides an enormous potential to easily extend this methodology to other regions worldwide. Any public strategy aimed at the sustainable use of marine resources should enhance the use of this kind of information technologies, collecting and archiving detailed information on the activity of all the vessels, especially in marine protected areas.

scholarly journals Using Spectrograms from Underwater Total Pressure Sensors to Detect Passing Vessels in a Coastal Environment

2020 ◽  
Vol 37 (8) ◽  
pp. 1353-1363
Author(s):  
Margus Rätsep ◽  
Kevin E. Parnell ◽  
Tarmo Soomere ◽  
Maarja Kruusmaa ◽  
Asko Ristolainen ◽  
...  
Keyword(s):  
Time Series ◽  
Water Flow ◽  
Pressure Sensors ◽  
Main Tool ◽  
Automatic Identification ◽  
Identification System ◽  
Transverse Waves ◽  
Multisensor Systems ◽  
Additional Information

AbstractMonitoring vessel traffic in coastal regions is a key element of maritime security. For this reason, additional ways of detecting moving vessels are explored by using the unique structure of their wake waves based on pressure measurements at the seabed. The experiments are performed at a distance of about 2 km from the sailing line using novel multisensor devices called “hydromasts” that track both pressure and near-bed water flow current velocities. The main tool for the analysis is a windowed Fourier transform that produces a spectrogram of the wake structure. It is shown that time series from the pressure sensors, measured at a frequency of 100 Hz, 0.2 m above the seabed are a valid source of input data for the spectrogram technique. This technique portrays the properties of both divergent and transverse waves with an accuracy and resolution that is sufficient for the evaluation of the speed and distance of the detected vessels from the measurement device. All the detected passings are matched with vessels using automatic identification system (AIS) data. The use of several time series from synchronized multisensor systems substantially suppresses noise and improves the quality of the outcome compared to one-point measurements. Additional information about variations in the water flow in wakes provides a simple and reasonably accurate tool for rapid detection of ship passages.

Automatic Identification System Receiver for Small Fishing Vessels

2020 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Fishing Vessels

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

scholarly journals Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

2021 ◽  
Vol 9 (4) ◽  
pp. 378
Author(s):  
Jong Kwan Kim
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Automatic Identification ◽  
Identification System ◽  
Spatial Statistical Analysis ◽  
Traffic Characteristics ◽  
Traffic Distribution ◽  
Traffic Flow Characteristics ◽  
Marine Accidents

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

scholarly journals Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 542-548
Author(s):  
Wenlong Tang ◽  
Hao Cha ◽  
Min Wei ◽  
Bin Tian ◽  
Xichuang Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication System ◽  
Particle Swarm Optimization Algorithm ◽  
Estimation Method ◽  
Particle Swarm ◽  
Automatic Identification ◽  
Identification System ◽  
Signal Power ◽  
Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

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