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A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories. / Wang, Xuan; Krasnov, Oleg; Deng, Jiahao ; Tran, Dihn.

The IET International Conference on Radar Systems 2017. IET, 2018. p. 1-4.

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Harvard

Wang, X, Krasnov, O, Deng, J & Tran, D 2018, A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories. in The IET International Conference on Radar Systems 2017. IET, pp. 1-4, Radar 2017: IET International Conference on Radar Systems, Belfast, United Kingdom, 23/10/17. https://doi.org/10.1049/cp.2017.0463

APA

Wang, X., Krasnov, O., Deng, J., & Tran, D. (2018). A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories. In The IET International Conference on Radar Systems 2017 (pp. 1-4). IET. https://doi.org/10.1049/cp.2017.0463

Vancouver

Wang X, Krasnov O, Deng J, Tran D. A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories. In The IET International Conference on Radar Systems 2017. IET. 2018. p. 1-4 https://doi.org/10.1049/cp.2017.0463

Author

Wang, Xuan ; Krasnov, Oleg ; Deng, Jiahao ; Tran, Dihn. / A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories. The IET International Conference on Radar Systems 2017. IET, 2018. pp. 1-4

BibTeX

@inproceedings{8abbe7c08e1d4d8abe08c4379765912c,
title = "A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories",
abstract = "In this paper, an imaging algorithm for the airborne radar system maneuvering along an arbitrary trajectory is proposed. The algorithm aims at wide-angle imaging with incomplete measurements from the nonlinear trajectory. The proposed composite joint sub-aperture imaging algorithm provides high reconstruction quality and supports efficient data collection policy. The image can be reconstructed by combining image patches corresponding to non-overlapping sub-apertures. The image patch is obtained by compressive sensing with joint sparse representation of the scene. Numerical results have proved that the proposed algorithm is highly effective and capable of image reconstruction without much loss in quality, especially on objects signature and contour.",
keywords = "Joint Sparsity, Compressive Sensing, Non-linear trajectory, Radar Imaging",
author = "Xuan Wang and Oleg Krasnov and Jiahao Deng and Dihn Tran",
year = "2018",
doi = "10.1049/cp.2017.0463",
language = "English",
isbn = "978-1-78561-673-0",
pages = "1--4",
booktitle = "The IET International Conference on Radar Systems 2017",
publisher = "IET",

}

RIS

TY - GEN

T1 - A Composite Joint Sub-aperture Imaging along Nonlinear Trajectories

AU - Wang, Xuan

AU - Krasnov, Oleg

AU - Deng, Jiahao

AU - Tran, Dihn

PY - 2018

Y1 - 2018

N2 - In this paper, an imaging algorithm for the airborne radar system maneuvering along an arbitrary trajectory is proposed. The algorithm aims at wide-angle imaging with incomplete measurements from the nonlinear trajectory. The proposed composite joint sub-aperture imaging algorithm provides high reconstruction quality and supports efficient data collection policy. The image can be reconstructed by combining image patches corresponding to non-overlapping sub-apertures. The image patch is obtained by compressive sensing with joint sparse representation of the scene. Numerical results have proved that the proposed algorithm is highly effective and capable of image reconstruction without much loss in quality, especially on objects signature and contour.

AB - In this paper, an imaging algorithm for the airborne radar system maneuvering along an arbitrary trajectory is proposed. The algorithm aims at wide-angle imaging with incomplete measurements from the nonlinear trajectory. The proposed composite joint sub-aperture imaging algorithm provides high reconstruction quality and supports efficient data collection policy. The image can be reconstructed by combining image patches corresponding to non-overlapping sub-apertures. The image patch is obtained by compressive sensing with joint sparse representation of the scene. Numerical results have proved that the proposed algorithm is highly effective and capable of image reconstruction without much loss in quality, especially on objects signature and contour.

KW - Joint Sparsity

KW - Compressive Sensing

KW - Non-linear trajectory

KW - Radar Imaging

U2 - 10.1049/cp.2017.0463

DO - 10.1049/cp.2017.0463

M3 - Conference contribution

SN - 978-1-78561-673-0

SP - 1

EP - 4

BT - The IET International Conference on Radar Systems 2017

PB - IET

ER -

ID: 34057601