Super-Resolution Technique of Multi-Radar Fusion 2D Imaging Based on ExCoV Algorithm in Low SNR

Round 1

Reviewer 1 Report

This manuscript presented a technique for imaging fusion using 2d-imaging super-resolution, which is helpful for radar image processing. The related methods are described in detail, but the results are a little weak by comparison. It would be better if the authors give more results to verify the proposed method. Besides, the references seem to be inadequate and imperfect, and English of this manuscript can be improved.

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Reviewer 2 Report

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Reviewer 3 Report

This paper proposes a new method of fusion imaging in the case of low signal-to-noise ratio. Based on the signal sparse theory, the expansion-compression variance-component algorithm is proposed to obtain the sparse parameters, which has particularly outstanding performance under low SNR condition.

 Overall，the article is well organized and its presentation is good. However, some minor issues still need to be improved:

1. In the introduction:

1) P1. Line 34:“In several studies, bandwidth extrapolation techniques are seen to improve range resolution”, The problems of existing methods in improving 2D resolution should be described in detail in Section I.

2) P2. Line 59: ‘Section VI’ should be ‘Section IV’ .

3) P1. Line 48: The selected signal model is important for the imaging algorithm, but there are currently many signal models, such as the GTD model selected in this paper, and the point target model, etc., please explain why the GTD model was chosen as the signal model of the algorithm in this article.

4) Some introducing on target imaging with moving platform radar would be interesting. Such as “Slow-Time FDA-MIMO Technique with Application to STAP Radar, IEEE Transactions on Aerospace and Electronic Systems 58 (1), 74-95”；“Enhanced three-dimensional joint domain localized STAP for airborne FDA-MIMO radar under dense false-target jamming scenario, IEEE Sensors Journal 18 (10), 4154-4166”；“Reconfigurable sparse array synthesis with phase-only control via consensus-admm-based sparse optimization, IEEE Transactions on Vehicular Technology 70 (7), 6647-6661”

2. P3. Line 53:“Every pixel grid can regard as a scattering center.”. In practice, not all goals fall exactly on the divided grid. If there are scattering points that fall between two grid points, how to deal with this situation, will the algorithm fail or can still achieve better imaging results through signal processing.

3. In this paper, the imaging method of azimuth fusion using two different angle observation signals is discussed, and whether the fusion imaging effect can still be achieved if the observation data from multiple angles is achieved.

4. Explain what factors are related to the resolution multiple that can be improved by using the super-resolution method proposed in this paper, and what is the upper limit of improvement.

5. It is suggested that at the end of this article, the future research outlook of the method requires a more detailed description.

Author Response

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