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Such sparse scene imaging problems to be considered herein are important in practice.
A high number of DOFs yield an improved resolution of the sparse scene.
Figure 12 Partially sparse scene reconstruction using cross-validation-based OMP.
This method exploits the group sparsity of the sparse scene, i.e., the observations associated with different bi-static pairs share the same support of the sparse scene but correspond to aspect-dependent scattering coefficients.
Sparse reconstruction can accurately reconstruct a sparse scene with very fewer Fourier samples, provided that the restricted isometry property (RIP) is satisfied [12, 13].
The performance of sparse scene SAR imaging depends on the available degrees of freedom (DOFs) of the radar system and the scene sparsity.
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The basics of the story are given interview-style, with the most telegenic parts recreated in sparse scenes.
The first appears due to the convolution of the transmitted waveform with the sparse target scene, spreading the energy of each target in the scene.
However, by assessing a sparse target scene, the homogeneous background influences the SSIM.
CSR was considered to transmit a sufficiently incoherent pulse and reconstruct the sparse target scene by the greedy algorithm.
To verify the effectiveness of the transmit waveform and sensing matrix optimization in random filtering structure-based CSR system, we assume that a sparse target scene has L=25 range and M=25 Doppler bins.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com