A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

A novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper to overcome the heterogeneous clutter. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method to improve the detection performance. Simulations and measured data validate the effectiveness of the proposed detection method.

However, there are still several problems that need to be addressed in this paper.

 

1. What is heterogeneous clutter? Please give a reasonable explanation.

 

2. Can the proposed method simultaneously detect and suppress many different types of clutter?

 

3. It is recommended to add some up-to-date references.

 

4. Are there errors in the axes in Figure 10(b)?

 

5. It is suggested to add additional evaluation indicators to the measured data, not just SCR.

Comments on the Quality of English Language

no

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1) I suggest a comparaisons with the detecors:

*Persymmetric Adaptive Normalized Matched Filter (P-ANMF).

*Recursive P-ANMF (RP-ANMF).

*Fixed Point Adaptive Normalized Matched Filter (FP-ANMF),

*Persymmetric Fixed Point Adaptive Normalized Matched Filter (PFP-ANMF) also called GLRT-PFP,

2) Add the references:

*E. Conte and A. De Maio, “Mitigation Techniques for Non-Gaussian Sea Clutter,” IEEE Journal of Oceanic Engineering, vol. 29, pp. 284–302, December 2003.
*F. Gini and M. V Greco, “Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter,” Signal Processing, special section on Signal Processing with Heavy Tailed Distributions,, vol. 82, pp. 1847–1859, December 2002.

*E. Conte, A. De Maio, and G. Ricci, “Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection,” IEEE Trans. on SP, vol. 50, pp. 1908–1915, August 2002.

*G. Pailloux, P. Forster, J.P. Ovarlez, and F. Pascal, “On Persymmetric Covariance Matrices in Adaptive Detection,” IEEE ICASSP 2008, pp. 2305–2308, April 2008.

*F. Pascal, Y. Chitour, J.P. Ovarlez, P. Forster and P. Larbazal,
“Covariance structure maximum likelihood estimates in compound-
Gaussian noise: existence and algorithm analysis”, IEEE Transactions
on Signal Processing, Vol. 56, No. 1, pp. 34-48, 2008.

3) Add some details on the existing methods of estimation of the covariance matrix of the clutter.

4)are the simulated data generated using Spherically Invariant Random Vectors (SIRV)? if so, mention it also the texture distribution used.

5) what is the objective of using CA-CFAR, SO_CFAR and GO-CFAR? this classical detector are used in non-coherent detection.

Comments on the Quality of English Language

-ommit the word 'about' in line 27

-add the signification of the abriviation HPD in line 89

-remove the abriviation 'Hermitian positive definite'' in line 145

-add the signification of CCM in line 250

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

I have no question.

Reviewer 3 Report

Comments and Suggestions for Authors

Accept in present form.