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Article
Peer-Review Record

A Robust Sparse Imaging Algorithm Using Joint MIMO Array Manifold and Array Channel Outliers

Remote Sens. 2022, 14(16), 4120; https://doi.org/10.3390/rs14164120
by Jieru Ding 1, Zhiyi Wang 1,2, Xinghui Wu 1 and Min Wang 1,*
Reviewer 1:
Reviewer 2: Anonymous
Remote Sens. 2022, 14(16), 4120; https://doi.org/10.3390/rs14164120
Submission received: 18 July 2022 / Revised: 13 August 2022 / Accepted: 18 August 2022 / Published: 22 August 2022
(This article belongs to the Special Issue Theory and Applications of MIMO Radar)

Round 1

Reviewer 1 Report

This paper studies MIMO radar imaging techniques. The paper needs to be carefully reviewed before submission, as it contains many mistakes. In addition, the evaluation must be improved. Please refer to the followings for more details.

1.      In the abstract, “due to its many advantages” and “it will directly not suit MIMO radar imaging” need to be explained more. What are the advantages exactly? Why is it not suitable?

2.      In the abstract, the “former” is stated that it can converge more accurately faster. Is it better than the latter in all aspects? The similar statement is near Table 5.

3.      A typo “liverse” in line 79.

4.      Section numbers usually start from 1, not 0.

5.      Figure 1 is placed in the middle of a sentence, degrading the readability.

6.      How large are the RAMs in the simulation computer?

7.      The computational complexities of the algorithms can be analyzed in a rigorous manner. How many arithmetic operations are involved in the algorithms?

8.      The algorithms can be more comprehensive if they can be accompanied with simple examples.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper proposes a sparse reconstruction imaging algorithm for MIMO radar by utilizing the sparsity of the scatter map in space and MIMO array manifold. Simulation results validate the effectiveness of the imaging algorithm.

On the whole, the current form of the paper is very difficult to meet the acceptance standard.

 1. Firstly, there are so many obvious and simple expression errors. More seriously, the first letters of many sentences are not capitalized. It seems that the paper was not carefully written.

 2. This paper mentions that the proposed algorithm can achieve a well performance even in presence of MIMO channel errors. However, the consideration for addressing the channel errors is not stated clearly in the algorithm. Besides, in the simulation, the channel errors are not considered as well.

 3. For the imaging results, some quantitative indicators need to be introduced to evaluate imaging quality.

 4. Please explain in detail why the traditional methods are completely ineffective for public data of Boeing-727 as shown in Figure 9 and 10. As a comparison, in Figure 8, the traditional methods are still effective for simulated data.

 5. Compared with the traditional methods, how does this method perform in terms of computation efficiency?

 6. What are the horizontal and vertical coordinates in Figure 2?

 7. The variables of many formulas lack the necessary explanation. For example, in Equation (4), what is the vector n0? Are the two variables (Textbf{E}m,n in Equation 11 and Ei,j in Equation 12) the same?

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The concerns have been addressed.

Reviewer 2 Report

The authors have addressed all my concerns, so I recommend the acceptance for this manuscript.

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