The Interpolation Method for the missing AIS Data of Ship

Nguyen, Van-Suong; Im, Nam-Kyun; Lee, Sang‐Min

doi:10.5394/kinpr.2015.39.5.377

Cited by 28 publications

(11 citation statements)

References 2 publications

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“…Lz et al (2015) presented a more sophisticated data interpolation method, which uses line, arc, and curve trajectories and is dedicated to inland AIS data. In other example, Nguyen et al (2015) used piecewise cubic Hermite interpolation. The values are presented by the SSPD metric (smaller is better).…”

Section: Discussionmentioning

confidence: 99%

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

et al. 2020

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The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network.

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Section: Discussionmentioning

confidence: 99%

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

et al. 2020

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“…To tackle the problem of trajectory gaps, several studies have focused on finding an optimal interpolation algorithm for movement data only in recent years, either in the maritime domain (Hu et al 2014, Sang et al 2015, Nguyen et al 2015, Du et al 2017 et al 2017) or not (Long 2015). Nguyen et al (2015) proposed a new method which includes linear interpolation and Cubic Hermit interpolation along with a mechanism to overcome problems of both approaches in order to fill in the gaps of missing AIS data. Hu et al (2014) developed an interpolation model which is able to accurately estimate the vessel's vectors of speed, heading and acceleration.…”

Section: Lagrange Interpolationmentioning

confidence: 99%

A distributed framework for extracting maritime traffic patterns

Kontopoulos

Varlamis

Tserpes

2020

International Journal of Geographical Information Science

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All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels' trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

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“…The interpolation is required for identifying ship position and route in a given time epoch, that is the epoch of the SAR data take. Many authors have focused their attention on selecting the most suitable technique for AIS data interpolation [8,[21][22][23]. Owing to the speed of computation and the capability of processing all the available information, the cubic Hermite spline is typically used.…”

Section: Ais Data Format Interpretation and Interpolationmentioning

confidence: 99%

Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness

2019

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The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship's velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship's velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.

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The Interpolation Method for the missing AIS Data of Ship

Cited by 28 publications

References 2 publications

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

A distributed framework for extracting maritime traffic patterns

Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness

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