Multi-Target CFAR Detection Method for HF Over-The-Horizon Radar Based on Target Sparse Constraint in Weibull Clutter Background
Round 1
Reviewer 1 Report
High frequency radar has a wide monitoring range and low range resolution, and may contain multiple targets or outlier interference phenomena in different clutter regions of the Range-Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Considering that the ordered statistics constant false alarm detector (OS-CFAR) detection method has good performance for multiple targets, this paper proposes a new method for multi-target OS-CFAR detection based on the sparse characteristics of the target. The effectiveness of the algorithm is analyzed using simulation and measured data. The results show that the algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions.
This is a well-written paper containing interesting results which merit publication. This is a carefully done study and the findings are of considerable interest. A few minor revision are list below.
Specific comments:
(1).It is recommended to add references within the last 5 years.
(2).It is recommended that the processing flow in Fig.2 be described in detail.
(3).Please add some definition of variables in formula (22) and (23).
(4).It is suggested to add the analysis part and conclusion of the measured data in Section 3.6, so as to help readers better understand the algorithm.
Author Response
Dear Editors and Reviewers:
Thank you for your kind letter on 21-Apr-2023, in which you informed us that our revised manuscript “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background” (remotesensing-2339865) has been provisionally accepted, but needs to be further modified and improved. Thank you very much for giving us an opportunity to further revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript. Those comments are all valuable and very helpful for revising and improving our paper. In the new revised version of the manuscript, we have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing.
This letter provides an item-by-item response to the reviewers’ comments. In the new revised manuscript, the revised words, sentences and equations have been marked with red color. Thanks very much again for your attention to our paper. Once again, thank you for your help to our paper processing.
On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript. Thank you very much for your time and effort to help me improving my paper, your comments have been to be helpful to me. We appreciate for your warm work earnestly, and hope that the correction will meet with approval.
Response to reviewer 1:
General comments
High frequency radar has a wide monitoring range and low range resolution, and may contain multiple targets or outlier interference phenomena in different clutter regions of the Range-Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Considering that the ordered statistics constant false alarm detector (OS-CFAR) detection method has good performance for multiple targets, this paper proposes a new method for multi-target OS-CFAR detection based on the sparse characteristics of the target. The effectiveness of the algorithm is analyzed using simulation and measured data. The results show that the algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions.
This is a well-written paper containing interesting results which merit publication. This is a carefully done study and the findings are of considerable interest. A few minor revision are list below.
Answer: On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript entitled “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background”. (ID: remotesensing-2339865).
Specific comments
(1)It is recommended to add references within the last 5 years.
Answer: We have added several references from the past five years and provided detailed explanations in the introduction section.
Changes: Reference，Page 19
- W. Zhou, J. Xie, G. Li and Y. Du, "Robust CFAR Detector With Weighted Amplitude Iteration in Nonhomogeneous Sea Clutter," in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, pp. 1520-1535, June 2017.
- I. Santamaria, L. L. Scharf and D. Ramírez, "Scale-Invariant Subspace Detectors Based on First- and Second-Order Statistical Models," in IEEE Transactions on Signal Processing, vol. 68, pp. 6432-6443, 2020.
- X.Hua et al., "LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023.
The above references were added.
(2) It is recommended that the processing flow in Fig.2 be described in detail.
Answer: We have made correction according to the Reviewer’s comments. We have added some necessary descriptions about Fig.2.
Changes: Section 2.4, Page 7,8.
Some detailed descriptions about Figure 2 have been added. As follows:
“The algorithm flowchart is shown in figure.2. In the simulation, Weibull clutter is first generated under set parameters, and a fixed number of targets are added to it. Then we introduce regularization processing, and stop updating the shape parameter when the cost function H converges. Given the false alarm probability, we obtain nominal factor T under this shape parameter. This value is combined with the indicator function during regularization processing to modify the value of k, thus obtaining the final detection threshold.”
(3)Please add some definition of variables in formula (22) and (23).
Answer: We have re-written this part according to the Reviewer’s suggestion. We have made some special markings regarding formula (22), (23)
Changes:
- Section 2.4, Page 7.
We have made some modifications to this paragraph, and the symbols appearing in the formula have been marked in italics. We hope this will help you understand.The revised paragraph is as follows:
“Assuming that in N length of the background clutter data, there are R targets with protection units positioned to the left and right sides, the number of unilateral protection units is set as R1 and the number of bilateral protection units is set as R2; then R1+R2=R. If the background clutter unit is sorted in ascending order, the target will exist in the tail (R+R1+2R2). Therefore, the following equation can be derived about the selection of k value when all targets and protection units are fully considered”
(4)It is suggested to add the analysis part and conclusion of the measured data in Section 3.6, so as to help readers better understand the algorithm.
Answer: We have re-written this part according to the Reviewer’s suggestion. We have added some detailed descriptions about the measured data.
Changes: Section 3.6, Page 15.
The statements of “There are often one or two more detection points compared with other detectors. Classic detectors that are used for comparison often fail to detect weak targets located in strong target occlusion areas. The main reason behind this is that when detecting weak target units, the presence of strong targets causes an increase in the detection threshold, making it hard to detect weak targets. ” were added.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper can be accepted after minor editing. In particular, please check: English language and style (sometimes sentences are too long and complex), references (re-order and avoid multiple repetitions, such as [8] and [17], [23] and [34], [24] and [38], etc.), figures (e.g., in Fig. 1 improve the descriptions provided in the caption by adding a) and b), add colorbar to a), etc.)
Author Response
Dear Editors and Reviewers:
Thank you for your kind letter on 21-Apr-2023, in which you informed us that our revised manuscript “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background” (remotesensing-2339865) has been provisionally accepted, but needs to be further modified and improved. Thank you very much for giving us an opportunity to further revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript. Those comments are all valuable and very helpful for revising and improving our paper. In the new revised version of the manuscript, we have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing.
This letter provides an item-by-item response to the reviewers’ comments. In the new revised manuscript, the revised words, sentences and equations have been marked with red color. Thanks very much again for your attention to our paper. Once again, thank you for your help to our paper processing.
On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript. Thank you very much for your time and effort to help me improving my paper, your comments have been to be helpful to me. We appreciate for your warm work earnestly, and hope that the correction will meet with approval.
Response to reviewer 2:
General comments
The paper can be accepted after minor editing. In particular, please check: English language and style (sometimes sentences are too long and complex), references (re-order and avoid multiple repetitions, such as [8] and [17], [23] and [34], [24] and [38], etc.), figures (e.g., in Fig. 1 improve the descriptions provided in the caption by adding a) and b), add colorbar to a), etc.)
Answer: On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript entitled “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background”. (ID: remotesensing-2339865).
Changes:
- We have made modifications to some too long and complex sentences in the paper.Belows are the detailed descriptions:
- Abstract, Page 1
The statements of“High frequency radar has a wide monitoring range and low range resolution, and may contain multiple targets or outlier interference phenomena in different clutter regions of the Range-Doppler (RD) spectrum in detected background.” were replaced by “High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the Range-Doppler (RD) spectrum in detected background. ”.
- Abstract, Page 1
The statements of“Based on this, considering that the ordered statistics constant false alarm detector (OS-CFAR) detection method has good performance for multiple targets, this paper proposes a new method for multi-target OS-CFAR detection based on the sparse characteristics of the target. ” were replaced by “ In this paper, we propose a new method for multi-target detection building on the Ordered Statistics Constant False Alarm Detector (OS-CFAR).”.
- Introduction, Page 2
The statements of“High-frequency over-the-horizon radar (OTHR) can detect over-the-horizon long-range targets and provide wide-range sea area monitoring by using the diffraction or reflection of high-frequency electromagnetic waves. ” were replaced by “ High-frequency over-the-horizon radar (OTHR) uses the diffraction or reflection of high-frequency electromagnetic waves can to detect over-the-horizon long-range targets and provide wide-range sea area monitoring.”.
- Section 2.4, Page 7
The statements of“Considering that the radar echo signal in the actual project is output as the bell envelope of Sa function after passing through the pulse matching filter [14], the target echo energy may occupy multiple resolution units, at this time, the target is no longer a point target, but an extended target occupying multiple units.” were replaced by “ As has been previously reported, when the radar echo signal passes through the pulse matching filter, the output fits the bell envelope of Sa function [14]. In this instance, the target echo energy may occupy multiple resolution units. It is no longer a point target, but an extended target occupying multiple units.”.
- We have reordered the references and reduced multipleAnd we have added several references on the latest methods of signal detection, and provided an overview in the introduction section.
- We have supplemented Figure.1 and provided different explanations for the two images, hoping to help you better understand.
Author Response File: Author Response.pdf
Reviewer 3 Report
In this paper, the authors present an OS-CFAR detection method in the multi-targets environment. Lots of experimental results are provided to verify the effectiveness of the proposed algorithm. I have some suggestions:
1. Abstract: it's better to condense the problem background, as the words of this part is less than 250.
2. It's better to add some advanced signal detection methods in the introduction part, such as the variation of CFAR [1], subspace detector [2], manifold detector [3].
[1] W. Zhou, J. Xie, G. Li and Y. Du, "Robust CFAR Detector With Weighted Amplitude Iteration in Nonhomogeneous Sea Clutter," in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, pp. 1520-1535, June 2017.
[2] I. Santamaria, L. L. Scharf and D. Ramírez, "Scale-Invariant Subspace Detectors Based on First- and Second-Order Statistical Models," in IEEE Transactions on Signal Processing, vol. 68, pp. 6432-6443, 2020.
[3] X. Hua et al., "LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023.
3. Please clarify why you consider the weibull clutter background.
4. Please add the outline of this paper in the last paragraph.
5. It's better to add more comparison results, such as the method in [1].
6. Please point out the potential limitations of the proposed method.
Author Response
Dear Editors and Reviewers:
Thank you for your kind letter on 21-Apr-2023, in which you informed us that our revised manuscript “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background” (remotesensing-2339865) has been provisionally accepted, but needs to be further modified and improved. Thank you very much for giving us an opportunity to further revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript. Those comments are all valuable and very helpful for revising and improving our paper. In the new revised version of the manuscript, we have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing.
This letter provides an item-by-item response to the reviewers’ comments. In the new revised manuscript, the revised words, sentences and equations have been marked with red color. Thanks very much again for your attention to our paper. Once again, thank you for your help to our paper processing.
On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript. Thank you very much for your time and effort to help me improving my paper, your comments have been to be helpful to me. We appreciate for your warm work earnestly, and hope that the correction will meet with approval.
Response to reviewer 3:
General comments
In this paper, the authors present an OS-CFAR detection method in the multi-targets environment. Lots of experimental results are provided to verify the effectiveness of the proposed algorithm. I have some suggestions:
Answer: On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript entitled “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background”. (ID: remotesensing-2339865).
Specific comments
(1)Abstract: it's better to condense the problem background, as the words of this part is less than 250.
Answer: We tried to use fewer words to clearly explain the work we had done. And we have re-written this part according to the Reviewer’s suggestion.
Changes: Abstract, Page 1
The statements of“High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the Range-Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Our previous research shows the number of targets belongs to an absolute minority compared to the number of background units. In this paper, we propose a new method for multi-target detection building on the Ordered Statistics Constant False Alarm Detector (OS-CFAR). The new method fully utilizes the sparse characteristics of the target and uses the idea of introducing regularization processing to eliminate interfering targets, and obtain an estimate of shape parameter for target detection. To further improve the performance of the algorithm, a correction method is proposed for the inaccurate selection of k value. After estimating the distribution parameter, the detection threshold is calculated, and the target’s constant false alarm detection is completed. Simulation and measured data show that our algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions, providing a reliable target detection method.” were added.
(2). It's better to add some advanced signal detection methods in the introduction part, such as the variation of CFAR [1], subspace detector [2], manifold detector [3].
[1] W. Zhou, J. Xie, G. Li and Y. Du, "Robust CFAR Detector With Weighted Amplitude Iteration in Nonhomogeneous Sea Clutter," in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, pp. 1520-1535, June 2017.
[2] I. Santamaria, L. L. Scharf and D. Ramírez, "Scale-Invariant Subspace Detectors Based on First- and Second-Order Statistical Models," in IEEE Transactions on Signal Processing, vol. 68, pp. 6432-6443, 2020.
[3] X. Hua et al., "LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023.
Answer: Based on your suggestion, we have carefully read these articles and provided a detailed summary of their content. We have included them in the references and summarized them in the introduction section.
Changes: Introduction, Page 3
The statements of“Zhou Weizhen proposed a weighted amplitude iteration (WAI)-CFAR method for Gamma (GM) distribution, which improves the detection performance under non-uniform clutter by weighting [21]. Santamaria used the subspace relationship of signals and adopted generalized likelihood ratio (GLR) to detect the statistical model [22]. Hua Xiaoqiang proposed four linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors, which used the principle of popular projection (manifold projection) to improve the robustness of the detector to anomalies [23].” were added.
(3). Please clarify why you consider the weibull clutter background.
Answer:Our research group has analyzed the data collected by three high-frequency radar systems in the early stage.These three systems include OTHR、bistatic HFSWR and HFSWR.We found through fitting analysis of the data that the Weibull distribution has good fit with the measured data.We introduced it in the second part, first section of the paper.We also include the analysis of Weibull clutter by some other researchers.
(4). Please add the outline of this paper in the last paragraph.
Answer: Based on your suggestion, we have carefully revised the last paragraph and added an outline to it.
Changes: Section 4, Page 17
The statements of“We derived the detection process of OS-CFAR under Weibull clutter background. In the case of unknown distribution parameters, we use regularization processing to eliminate interference targets in background clutter by utilizing the target's sparse characteristics and the amplitude information of background cells, and accurately estimate the shape parameter of background clutter. Considering the extension characteristics of actual engineering targets, the k-value is modified to obtain the final detection threshold. Simulation and measurement data show that the proposed BRACOS-CFAR can effectively resist interference from multiple targets and maintain good detection performance.” were added.
(5). It's better to add more comparison results, such as the method in [1].
Answer: We have carefully read the advice you provided. We are sorry that we did not add comparison results. We have carefully read these three literatures. We have carefully read these three literatures and found it difficult for us to complete the research of other researchers. And the radar data we used is from high-frequency over-the-horizon radar and the selected clutter background is Weibull clutter. Sorry again, we were unable to supplement according to your request.
(6). Please point out the potential limitations of the proposed method.
Answer: Our method targets the scenario of high-frequency radar with multiple targets, but the actual situation is still more complex. For scenes with clutter edges, our method may not have a good detection performance, which is a limitation. And we explained it at the end of the paper.
Changes: Section4, Page 17
The statements of “Although we have done a lot of research in multi-target scenarios, our method still has limitations in clutter-edge scenes, which is an area for future research.” were added.
Author Response File: Author Response.pdf
Reviewer 4 Report
The article presents an approach for detecting multiple targets using an improved threshold detection technique using a sparse constraint with a Weibull clutter background distribution. The research presents a novel solution for optimizing detection thresholds in the presence of clutter and jamming sources. The improvement in detection of multiple targets compared to traditional techniques shows great promise.
1 1. A lot of acronyms have been used in the manuscript such as HFSWR, CFAR, etc. It would be helpful to have an appendix of all the acronyms.
2. Reconstruct the sentence in lines 258 – 261. Sentence is long and complicated.
3. In Fig. 6, highlight the zone in (a) – the zoomed view of which is shown in (b). Based on this figure, the optimum value of k=32. It is shown that increasing the value of k (above 32), increases the threshold and reduces the detection performance. If the value of k is less than 32, the threshold would be lower, and this should also increase the rate of false alarms and reduce the detection performance? Please add additional plots in Fig. 6 with values less than k=32 to show the variation.
4. Based on Fig. 8 – it might be useful to add a separate figure with two more plots: (1) one showing how BRACOS performs for different shape parameters with number of targets kept constant. (2) Another showing how BRACOS performs for different number of targets with same shape parameter.
5. Fig. 10 – BRACOS seems to be able to identify additional weaker targets compared to other detection techniques. Were you able to verify that these were actual targets and not false alarms? Was the ‘ground truth’ on this data available? If so, were any targets that were present missed in the detection?
Author Response
Dear Editors and Reviewers:
Thank you for your kind letter on 21-Apr-2023, in which you informed us that our revised manuscript “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background” (remotesensing-2339865) has been provisionally accepted, but needs to be further modified and improved. Thank you very much for giving us an opportunity to further revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript. Those comments are all valuable and very helpful for revising and improving our paper. In the new revised version of the manuscript, we have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing.
This letter provides an item-by-item response to the reviewers’ comments. In the new revised manuscript, the revised words, sentences and equations have been marked with red color. Thanks very much again for your attention to our paper. Once again, thank you for your help to our paper processing.
On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript. Thank you very much for your time and effort to help me improving my paper, your comments have been to be helpful to me. We appreciate for your warm work earnestly, and hope that the correction will meet with approval.
Response to reviewer 4:
General comments
The article presents an approach for detecting multiple targets using an improved threshold detection technique using a sparse constraint with a Weibull clutter background distribution. The research presents a novel solution for optimizing detection thresholds in the presence of clutter and jamming sources. The improvement in detection of multiple targets compared to traditional techniques shows great promise.
Answer: On behalf of my co-authors, we thank you very much for giving us an opportunity to revise our manuscript, we appreciate editor and reviewers very much for their positive and constructive comments and suggestions on our manuscript entitled “Multi-target CFAR detection method for HF over-the-horizon radar based on target sparse constraint in Weibull clutter background”. (ID: remotesensing-2339865).
Specific comments
(1)A lot of acronyms have been used in the manuscript such as HFSWR, CFAR, etc. It would be helpful to have an appendix of all the acronyms.
Answer: Based on your suggestion, we have added an appendix to explain the acronyms that appear in the paper.
Changes: Appendix, Page 18
(2) Reconstruct the sentence in lines 258 – 261. Sentence is long and complicated.
Answer: We have carefully read the sentences you pointed out and made modifications. We greatly appreciate your careful reading of the sentence.
Changes: Section 2.4, Page 7
The statements of “Considering that the radar echo signal in the actual project is output as the bell envelope of Sa function after passing through the pulse matching filter [14], the target echo energy may occupy multiple resolution units, at this time, the target is no longer a point target, but an extended target occupying multiple units.”were replaced by “As has been previously reported, when the radar echo signal passes through the pulse matching filter, the output fits the bell envelope of Sa function [37]. In this instance, the target echo energy may occupy multiple resolution units. It is no longer a point target, but an extended target occupying multiple units.”
(3)In Fig. 6, highlight the zone in (a) – the zoomed view of which is shown in (b). Based on this figure, the optimum value of k=32. It is shown that increasing the value of k (above 32), increases the threshold and reduces the detection performance. If the value of k is less than 32, the threshold would be lower, and this should also increase the rate of false alarms and reduce the detection performance? Please add additional plots in Fig. 6 with values less than k=32 to show the variation.
Answer: Thank you very much for your question, it was our oversight on our part that we did not put the performance curve for K values less than 32 in the picture. Based on your suggestion, we have added to it. When the value of k is small, according to the formula, we know that under a constant false alarm probability, the nominal factor T will increase, which indirectly leads to an increase in the detection threshold and reduces detection performance.
Changes: Section 3.4, Page 11
We have added detection performance curves for Figure.6 with k values of 26 and 29, figure as shown in the paper.
(4)Based on Fig. 8 – it might be useful to add a separate figure with two more plots: (1) one showing how BRACOS performs for different shape parameters with number of targets kept constant. (2) Another showing how BRACOS performs for different number of targets with same shape parameter.
Answer: Based on your valuable suggestions, we have added figures f and g to the paper. And we provided a detailed explanation of the images. It can be seen that for the same detection probability, the larger shape parameter, the smaller the required SCR. For the shape parameter, as the number of targets increases, the performance of the detector slightly decreases, but overall it is acceptable.
Changes: Section 3.5, Page 13,14
We have added two figures in the paper.
(5)Fig. 10 – BRACOS seems to be able to identify additional weaker targets compared to other detection techniques. Were you able to verify that these were actual targets and not false alarms? Was the ‘ground truth’ on this data available? If so, were any targets that were present missed in the detection?
Answer: The experimental data is received from a bistatic high-frequency surface wave radar in Henan, China. After baseband signal processing, the received signal is converted to an azimuth–Range–Doppler (RD) map. We can see in Figure.9 that the RD spectrum contains more targets in the close range unit, we selected the distance profile for this scenario. So these data are available and we are sure that the BRACOS-CFAR detects actual weak targets. We have also verified the selected empirical data, there are 212 cells between −1 < Doppler (Hz) < 1 of the RD map. Among our selected distance profile, which includes ten strong and weak targets. We select four consecutive frames of data to examine the BRACOS-CFAR. Four consecutive frames of data were selected to examine the performance of BRACOS as well as the contrast detectors. And we didn't miss the targets. BRACOS-CFAR can detect ten targets in each frame of data.
Special thanks to you for your good comments. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
The authors have addressed all my concerns.
