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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2016.2535362
|2 doi
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|a pubmed24n0860.xml
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|a (NLM)26930684
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Xu, Gang
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a 3D Geometry and Motion Estimations of Maneuvering Targets for Interferometric ISAR With Sparse Aperture
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| 264 |
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|c 2016
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 02.08.2016
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|a Date Revised 24.03.2016
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Xing, Mengdao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xia, Xiang-Gen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Lei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Qianqian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bao, Zheng
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 25(2016), 5 vom: 01. Mai, Seite 2005-20
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:25
|g year:2016
|g number:5
|g day:01
|g month:05
|g pages:2005-20
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2016.2535362
|3 Volltext
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