Review Reports

Round 1

Reviewer 1 Report

1: The theoretical analyses is very less. I suggest the authors to give more.

2: About the used method or the related methods, I suggest the authors can give some equations. 

3: For the detection of the UAV, some new methods can be found in the references below.

[1] “An efficient strategy for accurate detection and localization of UAV swarms,” IEEE Internet of Things Journal, 2021.

[2] “Accurate detection and localization of unmanned aerial vehicle swarms-enabled mobile edge computing system,” IEEE Transactions on Industrial Informatics,  2021.

no

Author Response

Response to reviewer 1:

1: The theoretical analyses is very less. I suggest the authors to give more.

Response: Thanks for your suggestion. We add more theoretical analysis and details.

2: About the used method or the related methods, I suggest the authors can give some equations. 

Response: Thanks for your suggestion. We add more details, including the equations.

3: For the detection of the UAV, some new methods can be found in the references below.

[1] “An efficient strategy for accurate detection and localization of UAV swarms,” IEEE Internet of Things Journal, 2021.

[2] “Accurate detection and localization of unmanned aerial vehicle swarms-enabled mobile edge computing system,” IEEE Transactions on Industrial Informatics,  2021.

Response: Thanks for your suggestion. We add these references about drone swarms. Besides, we are working on some projects involving in detecting radar echoes from UAV swarms, and we will share our research in the future.

Reviewer 2 Report

In this paper, the authors propose a new method for the detection of small drones based on the Doppler Signal-to-Clutter Ratio (DSCR) detector. The presented results of the application of this method show that this method in combination with the ATR solution for the reduction of false alarms offers better performance in the detection of drones with a small RCS compared to classical methods based on traditional Signal-to-Noise (SNR) detectors. The theoretical model and the applied algorithm are described in a clear and consistent way. The experimental verification of the proposed method was also done in a correct and consistent manner. The results obtained by simulation as well as the results obtained in the field (real Ku-band and X-band radar data) demonstrate the ability of DSCR detectors to detect the weak radar signal of the drone in the clatter. Based on all of the above, the paper contains a satisfactory scientific contribution that is necessary for publication in a journal.

Author Response

Response to reviewer 2:

In this paper, the authors propose a new method for the detection of small drones based on the Doppler Signal-to-Clutter Ratio (DSCR) detector. The presented results of the application of this method show that this method in combination with the ATR solution for the reduction of false alarms offers better performance in the detection of drones with a small RCS compared to classical methods based on traditional Signal-to-Noise (SNR) detectors. The theoretical model and the applied algorithm are described in a clear and consistent way. The experimental verification of the proposed method was also done in a correct and consistent manner. The results obtained by simulation as well as the results obtained in the field (real Ku-band and X-band radar data) demonstrate the ability of DSCR detectors to detect the weak radar signal of the drone in the clatter. Based on all of the above, the paper contains a satisfactory scientific contribution that is necessary for publication in a journal.

Response: Thank you for your review. We appreciate your recognition of the effectiveness of our method, which has been extensively tested in multiple radar systems over several years to provide ample verification of its performance.

Reviewer 3 Report

In this paper, the use of a Doppler signal-to-clutter ratio (DSCR) detector in order to extract both amplitude and Doppler information from drone signals, is presented. Given a vector containing the radar echoes raw data, its FFT is calculated and the corresponding maximum value, as well as the position (starting from an intermediate component M) of this maximum value, is determined. This method is extremely simple and the proposed methodology has limited technical innovations. As a result, I suggest the rejection of the paper.  

Concerning the quality of English language, the presentation is quite satisfactory.

Author Response

Response to reviewer 3:

Comments and Suggestions for Authors

In this paper, the use of a Doppler signal-to-clutter ratio (DSCR) detector in order to extract both amplitude and Doppler information from drone signals, is presented. Given a vector containing the radar echoes raw data, its FFT is calculated and the corresponding maximum value, as well as the position (starting from an intermediate component M) of this maximum value, is determined. This method is extremely simple and the proposed methodology has limited technical innovations. As a result, I suggest the rejection of the paper.  

Response: Thank you for your review. While the DSCR method may seem simple, it has proven to be highly effective. The theoretical basis of this method is derived from both Doppler theory and micro-Doppler theory, and we have tested it using real radar data within the Ku-band and X-band. We respectfully disagree with the comment regarding limited technical innovations, as to our knowledge, no similar detector existed in radar engineering before our work. Furthermore, we have utilized this method in several radar systems, and years of testing have provided ample evidence of its effectiveness. While the DSCR method may not yet be widely understood in the radar engineering community, we believe it has the potential to be a revolutionary parameter and detector, as it effectively reduces missed targets when detecting objects with small RCS values such as drones. Overall, we stand by the theoretical rigor and methodological efficiency of the DSCR method.

Round 2

Reviewer 3 Report

As I have reported in my first review, from the aspect of the Signal Processing or Machine Learning field, the technical innovations of the paper are extremely limited. On the other hand, taking into account that the aim of this paper is to present a novel method exclusively related to drones, motivated by the Doppler and micro-Doppler theory, which is accompanied by extensive experimentation, I finally agree in the acceptance of the paper.