Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

Round 1

Reviewer 1 Report

In this article, the authors fully consider the heterogeneity of clutter in real-world environments and propose a dimensionality reduction STAP method based on sparse Bayesian learning, which can achieve suboptimal clutter suppression performance using only a single sample. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used. I believe that publication of the manuscript may be considered only after the following issues have been resolved.

1.    The introduction section needs to reduce background explanation and provide more personal content, especially the exploration of relevant physical mechanisms.

2.    In order to highlight the advantages of this work, it is recommended that the author supplement a comparative table of related work.

3.    To improve the accuracy of the image, it is necessary to add relevant calculation points in Figure 5.

4.    The English expression of the whole article needs to be further improved.

Minor editing of English language required.

Author Response

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Reviewer 2 Report

This manuscript explains how to overcome the challenges in airborne radar systems, particularly in detecting targets with the presence of ground clutter. Traditional STAP methods require a large number of training samples to estimate the clutter plus noise covariance matrix. The authors proposed a BL-based algorithm to estimate the CNCM with only a single training sample. The method is well-written, and the results seem to be well-supported: the SBL-based RD STAP method significantly enhances the detection performance of moving targets in the ground clutter. It seems the method may be highly practical and efficient.

However, the paper would greatly benefit from incorporating practical, real-world measurements to validate the method, including either actual field tests or a future implementation plan in radar systems. The results are solely backed up by simulations, without tangible experimental verification, which stands as a notable limitation of the study. I recommend the authors provide an experimental verification. If not available, a plan for future empirical testing methods with an actual radar system should be stated.

Author Response

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Reviewer 3 Report

The manuscript titled “Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning” proposes a novel approach that leverages a sparse Bayesian learning-based reduced dimension STAP method to achieve clutter suppression performance using only a single snapshot. The manuscript's motivation is well presented, the methodology is clearly explained, and the results are sufficiently insightful to recommend publication of the paper. However, some minor comments outlined below need to be considered. However, some minor comments outlined below need to be considered.

11)          What are the drawbacks of employing a clutter suppression algorithm that utilizes only one snapshot, aside from the evident impact on the signal-to-noise ratio?

22)     Could the authors theoretically demonstrate, through mathematical formulation, why the Signal-to-Interference-plus-Noise Ratio (SINR) of the proposed method surpasses that of the traditional SR STAP?

33)     This reviewer raises a concern about whether the decreased computational load associated with requiring only one snapshot is offset by an increase in the number of mathematical operations required by the proposed algorithm. It would be valuable to compare the computational complexity of the proposed approach with that of other similar algorithms presented in the manuscript. Perhaps, the authors can utilize MATLAB to estimate the runtime of various clutter suppression algorithms including the proposed approach.

Author Response

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Reviewer 4 Report

Overall the work presented is very structured and scientific relevant. Only some minor recommendations:

-exploring the algorithm's performance in different operational scenarios and against various types of clutter and distributions. Or at least mention the limitations under different clutter distributions.

-maybe point out in text approaches to recursively adapt the covariance matrix

Author Response

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Reviewer 5 Report

[Title]

Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

[sumary]

The authors propose an angular Doppler domain reduced dimension STAP algorithm based on SBL.

They describe the algorithm and demonstrate the performance of it by numerical similations.

[Broad Comment]

The presentation si clear and I have only one major comment.

It may be better to refer to the applicability (or, possible future work) of this algorithm in the conclusion section.

[Specific Comments]

Figure 5

 Denote what color bars represent.

The English quality is fine, but there are some minor careless misses.

In Section 4:

 Figure. 5a -> Figure 5a

 figure. 11 -> Figure 11

Author Response

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Round 2

Reviewer 1 Report

Accept.