Novel Approach for InSAR Sensors Imaging via Gradient-Based Algorithm for the Sparse Signal Reconstruction

Wang, Yong; Qian, Guangzhao

doi:10.1109/jsen.2018.2791568

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…However, such a 3D-FT based method requires a measurement of radar data in three dimensions with a dense sampling way, which increases the cost of computation and storage significantly. The second type of approaches is based on the interferometric ISAR (InISAR) [4][5][6][7], which uses the spatial information provided by multiple antennas. Such an approach has the advantage of not requiring the prior knowledge of target motions.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional reconstruction for space targets with multistatic inverse synthetic aperture radar systems

Zhao

Zhang

Jiu

et al. 2019

EURASIP J. Adv. Signal Process.

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In this paper, a three-dimensional (3D) reconstruction algorithm is proposed for space targets with multistatic inverse synthetic aperture radar (ISAR) systems. In the proposed algorithm, target 3D geometry can be obtained by solving the projection equations between the target 3D geometry and ISAR images. Specially, it is no need to perform cross-range scaling. To obtain the projection equations, the algorithm consists of two steps: establishing projection matrix and associating scattering centers. Firstly, observation angles of sensor can be estimated by kinematic formulas and coordinate systems transformation. Using azimuth and elevation angles of sensor relative to target, the projection matrix from target 3D geometry to ISAR images is established. Secondly, an association cost function based on projective transform and epipolar geometry is developed. As the cost function is an assignment with 0-1 linear programming, the Jonker-Volgenant algorithm is used to build a one-to-one correspondence between two scattering centers. Numerical results show the efficiency of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%