Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing
Round 1
Reviewer 1 Report
see attached file.
Comments for author File: 
Comments.pdf
Author Response
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Author Response File: Author Response.pdf
Reviewer 2 Report
Original Submission
Recommendation
Minor corrections
Comments to Author:
I Put the review in the attachment
Comments for author File: Comments.pdf
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
in the paper entitled " Fast, efficient and viable Compressed Sensing, Low-Rank and Robust Principal Component Analysis Algorithms for Radar Signal Processing" the authors propose algorithms: TST and its refinement CSCA for Compressed Sensing, TSVT and CSRA as improved algorithm for Affine Rank minimization problems and finally a hybrid solution based on the previous suggested algorithms.
The author's work is of good quality and is based on a solid theoretical background. The results presented support the author's claims in terms of practical use and processing time of the proposed algorithms for radar signal processing.
General note:
Please add a reference or footnote for the Two Github repositories mentioned.
Questions:
in Section 3.1 TSVT:
Figure 9: Is there an explanation as to why the reconstruction performance of TARM (R=p) and TSVT is poor for higher degree of freedom in rank-p matrix only for DFT operators ?
Figure 11: the TSVT seems to converge half as fast as the SVP algorithm for DFT operator ( ~12 iterations for SVP and 37 iterations for TSVT), can you explain why, knowing that TSVT and TARM performance seems similar?
Section 4.1. TCRPCA:
Algorithm 5 and algorithm 6. please check the value of 'epsilon'
Author Response
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Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
the comments are all addressed. we look forward to the later publications of the proposed method in real world application.
