A Parametric 3-D ISAR Imaging Method of Celestial Target Under Low SNR.

IEEE Trans. Geosci. Remote. Sens.(2023)

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摘要
The 3-D inverse synthetic aperture radar (ISAR) imaging technology is widely used for noncooperative targets, which can obtain precise topography, structure, and rotation information of celestial target. However, celestial target observation has the features of long observation distance, low echo signal-to-noise ratio (SNR), and complex rotation characteristic, which severely degrades the performance of traditional 3-D ISAR methods. Therefore, in order to realize high-precision 3-D ISAR imaging of celestial target, a parametric 3-D ISAR imaging method is proposed in this article. First, a 3-D imaging model is established based on the complex rotation characteristic of the celestial target, which indicates that the rotation vector and polar diameter estimation is the key to 3-D reconstruction. Second, in order to overcome the performance degradation of traditional 3-D ISAR methods under low SNR, a parameter estimation method based on hybrid generalized radon-Fourier transform (HGRFT) is proposed, and the core is to use GRFT within subaperture and noncoherent accumulation between subapertures to achieve hybrid accumulation of echo signals, so as to obtain the desired rotation vector and polar diameter fast and accurately. Consequently, the 3-D reconstruction of the celestial target under low SNR can be achieved based on the 3-D imaging model and the parameter estimation results. Moreover, two fast implementations based on parameter search space dimensionality reduction and heuristic search, respectively, are proposed, which can reduce the computational load of high-dimensional space parameter estimation and further improve the algorithm efficiency. Finally, the proposed method is validated by celestial target 3-D ISAR imaging simulation.
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关键词
3-D inverse synthetic aperture radar (ISAR) imaging, accurate parameter estimation, celestial target observation, high-precision 3-D reconstruction, low signal-to-noise ratio (SNR)
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