Doppler-Spread Space Target Detection Based on Overlapping Group Shrinkage and Order Statistics

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper presents a Doppler-spread space target detection approach based on equidistant sparse characteristics of its Doppler domain signal. The main idea is to first estimate the strong Doppler cells using sparse recovery, and then integrate these estimates into the GLRT for detection. Overall, the idea is somewhat new and the structure of this paper is clear. I think the paper deserves to be published if the authors can address the following comments.

 

-- The authors should enhance the reliability of this paper by adding references, particularly in the first paragraph of the introduction and the signal model section.

 

--The authors claim that the Doppler spectrum of Doppler-spread targets has equidistant sparse characteristics, yet the paper lacks an explanation for this phenomenon. Thus, please clarify and further explain in detail.

 

-- The constant false alarm rate (CFAR) characteristic of the detection method should be verified. Therefore, I would recommend that the authors conduct a Monte Carlo simulation to examine the false alarm probability under different noise power conditions.

 

-- In simulations, the performance of the proposed method has not been discussed for a point-like target model (without Doppler spread). It is necessary to analyze the performance limitation of the proposed method.

Comments on the Quality of English Language

should be modified.

Author Response

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Reviewer 2 Report

Comments and Suggestions for Authors

In order to address Doppler spread problem in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized, this paper proposes an interval-adaptive OGS-based denoising algorithm for Doppler spread line spectra with equidistant sparsity and a GLRT based on the order statistics of a denoised Doppler sequence for target detection. The simulations show that the proposed algorithm has generally good performance compared to some typical GLRT-based detectors. But I still have some concerns.

 

1、The introduction should be expanded to include a more detailed discussion of the limitations of existing methods, which highlight the improvements offered by the proposed OGSos-GLRT method.

 

2、The OGSos-GLRT detector is compared with only three typical GLRT-based detectors. What are the advantages of the algorithm compared to latest algorithms?

 

3、What are the effects of the other parameters and the basis for their selection in Section 5, e.g. pulse repetition time, probability of false alarm, number of Monte Carlo simulations etc.?

 

 

4、Please demonstrate the effectiveness of the proposed expression of false alarm probability.

 

5、Besides Model 3, the detection curves of algorithms in the match cases for other models are suggested to be shown in Figure 7.

 

6、It would be better to validate the performance of the OGSos-GLRT detector via real remote sensing datasets.

 

7、The paper does not adequately discuss the limitations of OGSos-GLRT.

 

8、Why doesn't the paper include an analysis or discussion of the convergence rate of the proposed algorithm?

Author Response

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Reviewer 3 Report

Comments and Suggestions for Authors

The article is oriented to the issue of target detection. In this study, the problem of Doppler propagation is specifically addressed. The topic of the article is topical considering the current state of knowledge in this area.
In the introduction, the authors document the current state in this area and describe the problems that are solved in this detection. Links to relevant publication references are also provided.
The main contribution achieved in this study to this field is also described.
In the next part of the article, the signal model is described and also the fundamental theory of the overlapping group shrinkage (OGS) algorithm is briefly introduced. The OGS-based interval-adaptive denoising algorithm is presented using pseudo-code.
The principle and schematic of the proposed detector is also shown later in the article.
The results of the simulations are presented and there is also a comment on the individual graphic outputs of the simulations.
In the end, the results of this study are summarized and the future research plan is also presented.

The article is very well made and I did not find any serious flaws or errors in it. I recommend publishing the article.

Comments:
in pictures 5, 6, 7, 9, 10, 11, 13, also provide a verbal description of the axis of the graphs, as it is in pictures 1, 2 and 3.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

My questions have been settled. However, the false alarm probability could remain constant or fluctuate around the preset value under different noise power levels. Therefore, please the authors check and modify Fig.5 (b).

Comments on the Quality of English Language

None

Author Response

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Author Response File: Author Response.pdf