Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88930
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorHe, ZY-
dc.creatorYang, Y-
dc.creatorChen, W-
dc.creatorWeng, DJ-
dc.date.accessioned2021-01-15T07:14:08Z-
dc.date.available2021-01-15T07:14:08Z-
dc.identifier.urihttp://hdl.handle.net/10397/88930-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication He, Z.-Y.; Yang, Y.; Chen, W.; Weng, D.-J. Moving Target Imaging Using GNSS-Based Passive Bistatic Synthetic Aperture Radar. Remote Sens. 2020, 12, 3356 is available at https://dx.doi.org/10.3390/rs12203356en_US
dc.subjectGNSS-SARen_US
dc.subjectImage formation algorithmen_US
dc.subjectKeystone transformen_US
dc.subjectRandom sample consensusen_US
dc.subjectShort-time fourier transformen_US
dc.titleMoving target imaging using GNSS-based passive bistatic synthetic aperture radaren_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage21-
dc.identifier.volume12-
dc.identifier.issue20-
dc.identifier.doi10.3390/rs12203356-
dcterms.abstractCurrent studies of global navigation satellite systems (GNSS)-based bistatic synthetic aperture radar (GNSS-SAR) is focused on static objects on land. However, moving target imaging is also very significant for modern SAR systems. Imaging a moving target has two main problems. One is the unknown range cell migration; the other is the motion parameter estimation, such as the target’s velocity. This paper proposes a moving target imaging formation algorithm for GNSS-SAR. First, an approximate bistatic range history is derived to describe the phase variation of the target signal along the azimuth time. Then, a keystone transform is employed to correct the range cell migration. To address the motion parameter estimation, a chirp rate estimation method based on short-time Fourier transform and random sample consensus is proposed with high processing efficiency and robust estimation errors in low signal-to-noise ratio scenes. The estimated chirp rate can calculate the target’s velocity. Finally, azimuth compression derivation is performed to accomplish GNSS-SAR imaging. A maritime experimental campaign is conducted to validate the effectiveness of the proposed algorithm. The two cargo ships in the SAR images have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range and the ships’ length along the cross-range.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, 2 Oct. 2020, v. 12, no. 20, 3356, p. 1-21-
dcterms.isPartOfRemote sensing-
dcterms.issued2020-10-02-
dc.identifier.scopus2-s2.0-85092928426-
dc.identifier.eissn2072-4292-
dc.identifier.artn3356-
dc.description.validate202101 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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