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Sparse Bayesian 3-D Imaging for Low-RCS Objects via Dyadic Green's Function
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DOI：10.1109/LAWP.2021.3090052.png)
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摘要
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Sparse Bayesian (SB) 3-D imaging resolution transcends classical radar cross section (RCS) imaging, providing a novel solution for low-RCS objects. To better resolve low-RCS objects, we propose an improved SB 3-D imaging algorithm via dyadic Green's function, i.e., 1) in the building of the radar observation model, a novel dyadic Green's function-form dictionary (DGFD) is designed to replace traditional scalar form, contributing to resolving low-RCS objects; 2) in the implementation of 3-D imaging, an improved SB algorithm using DGFD is proposed, further ensuring high precision. Electromagnetic simulation results show the precision of the proposed algorithm is less than 1 dB in mean absolute deviation and less than 1.5 dB in standard deviation, better than existing algorithms.
Keyword:
Three-dimensional displays
Imaging
Radar imaging
Solid modeling
Dictionaries
Radar cross-sections
Radar antennas
Dyadic Green's function
low-radar cross section (RCS) objects
sparse Bayesian (SB) 3-D imaging
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4.8
论文数:
1.0W
被引数:
2.8W
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Three Dimensional Image-Based Radar Cross Section Extrapolation via Planar Projective Transforms.png)
IEEE ACCESS
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IEEE ACCESSIF3.6
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