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In Section 3, the proposed method is described in detail for our primary approach, its extensions based on group sparsity and low-rank sparse decomposition, and the ROI-based approach.
We, therefore, propose a DOA estimation approach based on group sparsity for the nonproportional spectra case.
On the contrary, the sequential method based on group sparsity is robust to such phase interactions, as it only assumes that a common sparsity support is shared among all available bistatic links.
On the other hand, the two-step sequential method based on group sparsity is robust against such phase discrepancies because the performance of the group sparsity-based method does not directly depend on the coherent combination of the multiple Doppler signatures.
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Community organizing is based on group participation.
3. Estimate the source representation based on the sparsity assumption.
Based on the sparsity of various frequency components, the coefficients are sampled at different sampling rates.
Section 3 presents the super-resolution algorithm based on spatial sparsity and endmember regularization.
Based on this sparsity prior, we formulate the sparse representation regularization term and incorporate it into the PR optimization problem.
We consider a separation criterion based on the sparsity of the signals in the time-frequency domain.
The method applied overcomes these difficulties by imposing a multi-level substructuring procedure, based on the sparsity pattern of the stiffness matrix.
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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