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SAR and AIS Fusion for Maritime Surveillance

Achiri, Lotfi, Guida, Raffaella and Iervolino, Pasquale (2018) SAR and AIS Fusion for Maritime Surveillance In: 4th International Forum on Research and Technologies for Society and Industry, 10-13 Sep 2018, Palermo, Italy.

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Abstract

This paper presents a novel approach to fuse Synthetic Aperture Radar (SAR) images and Automatic Identification System (AIS) data for maritime surveillance. The procedure consists of four steps. First, ship detection is performed in the SAR image using a Constant False Alarm Rate (CFAR) algorithm; then feature extraction (ship position, heading and size) is performed on ships detected in the SAR image, the third step consists in identifying the detected ships and extracting the same features from the AIS data. The final step is to feed the fusion block with both features vectors extracted separately from the SAR and AIS. Here the arithmetic mean function is established. The algorithm is tested using simulated SAR images and AIS data. Preliminary results of the fusion of SAR and AIS data are presented and discussed.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Achiri, Lotfil.achiri@surrey.ac.uk
Guida, RaffaellaR.Guida@surrey.ac.uk
Iervolino, Pasqualep.iervolino@surrey.ac.uk
Date : September 2018
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : SAR; AIS; Ship detection Ship monitoring; Data fusion; Maritime surveillance
Related URLs :
Depositing User : Clive Harris
Date Deposited : 03 Oct 2018 09:45
Last Modified : 03 Oct 2018 09:45
URI: http://epubs.surrey.ac.uk/id/eprint/849512

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