Interpolation Between AIS Reports: Probabilistic Inferences Over Vessel Path Space

2011 ◽  
Vol 64 (4) ◽  
pp. 595-607 ◽  
Author(s):  
D. J. Peters ◽  
T. R. Hammond
Keyword(s):  
Path Space ◽  
Automatic Identification ◽  
Identification System ◽  
Time Interval ◽  
Random Generation ◽  
Automatic Identification System ◽  
Illegal Dumping ◽  
True Path ◽  
Probabilistic Queries

We present a method for addressing probabilistic queries about the location of a vessel in the time interval between two position reports, such as from the Automatic Identification System (AIS). The heart of the method is the random generation of physically feasible paths connecting the two reports. The method empowers operators to answer probabilistic questions about any characteristic of the unknown true path. For illustrative purposes, we demonstrate the use of the method to identify which of several vessels is the most likely perpetrator, in a fictitious scenario in which illegal dumping of waste matter has taken place.

scholarly journals Automatic Identification System based Fishing Trajectory Data Preprocessing Method using Map Reduce

2019 ◽  
Vol 8 (2S6) ◽  
pp. 352-356
Keyword(s):  
Radio Channel ◽  
Automatic Identification ◽  
Identification System ◽  
Time Interval ◽  
Fishing Gear ◽  
Trajectory Data ◽  
Test Dataset ◽  
Automatic Identification System ◽  
Proposed Model ◽  
Types Of Information

Many countries use vessel monitoring system (VMS) data to monitor their fishery activities. However, VMS data is limited in terms of distinguishing operations involving illegal fishing gear. Recently introduced automatic identification system (AIS) data is advantageous for tracking fishing ship behaviors.AIS data include various types of information about a ship, such as its state of navigation and its broadcast rate on the radio channel. We interpolate AIS trajectory data with a regular time interval and extract the ship velocity and course change data for fishing ship gear classification. To simplify and condense the data, the course change index (CCI) and ship speed index (SSI) are applied to the ship velocity and course data. The proposed mapper combines CCIs and SSIs into key words, while the proposed reducer collects fishing ship gear type values that are of the same key.By using the proposed key-value dataset from the MapReduce procedure, we can classify fishing gear type. We evaluated the performance of the proposed model by using a test dataset. The results showed that the proposed model achieved 76.2% accuracy in the classification of fishing ship trajectories against the test dataset.

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.

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