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

MAXIMILIAN BRYANT RODGER; RAFFAELLA GUIDA

doi:10.1109/IGARSS.2019.8898207

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

Conference proceeding

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

MAXIMILIAN BRYANT RODGER and RAFFAELLA GUIDA

2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) (Yokohama, Japan, 28/07/2019 - 02/08/2019)

14/11/2019

DOI: https://doi.org/10.1109/IGARSS.2019.8898207

Abstract

Index Terms— synthetic aperture radar, SAR, automatic

identification system, AIS, data fusion, data association,

space-based maritime surveillance, NovaSAR-1

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 el-lipsoid. 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.

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Title: Data association techniques for near-contemporaneous SAR and AIS datasets from NovaSAR-1
Creators: MAXIMILIAN BRYANT RODGER (Author) - University of Surrey, School of Computer Science and Electronic Engineering
RAFFAELLA GUIDA (Author) - University of Surrey, School of Computer Science and Electronic Engineering
Conference: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) (Yokohama, Japan, 28/07/2019 - 02/08/2019)
Date published: 14/11/2019
Date accepted: 05/04/2019
Identifiers: 99520123202346
Copyright: © 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.
Academic Unit: School of Computer Science and Electronic Engineering
Language: English
Resource Type: Conference proceeding

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

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