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Data association techniques for near-contemporaneous SAR and AIS datasets from NovaSAR-1

Rodger, Maximillian and Guida, Raffaella (2019) Data association techniques for near-contemporaneous SAR and AIS datasets from NovaSAR-1 In: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 28 Jul - 02 Aug 2019, Yokohama, Japan.

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Abstract

In this research the best techniques of fusion for near-contemporaneous Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) datasets are studied to simulate the expected performance from NovaSAR-1. Specifically, data association techniques are quantitatively compared by performing a series of Monte Carlo tests. The evaluation has been carried out using a satellite-based AIS dataset acquired from the English Channel on 07 June 2016, and SAR ship detections are simulated by reckoning the AIS dataset forward in time along a geodesic on a WGS84 reference ellipsoid. Accurate data association is achieved using an m-best multidimensional assignment technique, which is consistent with being used in an operational environment, especially in high-density shipping areas.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Rodger, Maximillianm.rodger@surrey.ac.uk
Guida, RaffaellaR.Guida@surrey.ac.uk
Date : 2019
Copyright Disclaimer : © 2019 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 : Synthetic aperture radar; SAR; Automatic identification system; AIS; Data fusion; Data association; Space-based maritime surveillance; NovaSAR-1
Related URLs :
Depositing User : Clive Harris
Date Deposited : 20 Aug 2019 15:06
Last Modified : 28 Oct 2019 13:53
URI: http://epubs.surrey.ac.uk/id/eprint/852451

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