Please use this identifier to cite or link to this item:
|Title:||Knowledge based vessel position prediction using historical AIS data|
|Authors:||MAZZARELLA FABIO; FERNANDEZ ARGUEDAS VIRGINIA; VESPE MICHELE|
|Type:||Articles in periodicals and books|
|Abstract:||The improvement in Maritime Situational Awareness (MSA), or the capability of understanding events, circumstances and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. Enhancing coverage of existing technologies such as Automatic Identification System (AIS) provides the possibility to integrate and enrich services and information already available in the maritime domain. In this scenario, the prediction of vessels position is essential to build the map of the ships located in a certain area of interest. The integration of de-facto maritime traffic routes information in the vessel prediction process has the appealing potential to provide a more accurate picture of what is happening at sea by exploiting the knowledge of historical vessel positioning data. In this paper, we propose a Bayesian vessel prediction algorithm based on a Particle Filter (PF). The system, aided by the knowledge of traffic routes, aims to enhance the quality of the vessel position prediction. Experimental results are presented, evaluating the algorithm in the specific area between the Gibraltar passage and the Dover Strait using real AIS data.|
|JRC Directorate:||Space, Security and Migration|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.