A Unified Algorithm for the Sliding Spotlight and TOPS Modes Data Processing in Bistatic Configuration of the Geostationary Transmitter with LEO Receivers
Round 1
Reviewer 1 Report
The authors describe s bistatic RADAR geometry using a geostationary satellite as source and a LEO receiver. The signal model is developed, various artifacts related to Doppler shift aliasing are removed, and results are modeled demonstrating the soundness of the procedure. The paper is overall well written extensively refers to the literature, and the processing flow is clearly presented.
My only comment is about the purely theoretical analysis with no experimental assessment of the validity of the results. Of course assembling a LEO receiver to a GEO emitter is not a trivial task, but I wonder how the author's model could be related to https://www.mdpi.com/2072-4292/14/1/221 where the LEO source and static receiver might represent the static emitter and LEO receiver. Could the formalism developed by the authors be applied to such a geometry to demonstrate its validity? Since the Sentinal1 raw IQ datasets are available from the European Space Agency web site, could a GEO emitting in the C-band be received by this LEO satellite for demonstrating the proposed algorithm? Indeed during the rank echo periods as described at https://mdpi-res.com/d_attachment/remotesensing/remotesensing-09-01183/article_deploy/remotesensing-09-01183.pdf incoming signal sources might be detected without being overwhelmed with the returned signal echoes.
Trivial:
l.50, p.2: "Since" the Doppler contribution ... (otherwise this sentence sound awkward with little relation between the first half "The Doppler" ... and the second half "the corresponding imaging..."
l.428 p.15: is aliasing -> is aliased
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of geostationary (GEO) transmitter with low earth orbit satellite (LEO) receiver. The authors propose a unified frequency-domain imaging algorithm. Overall, the paper is interesting and the results are promising. I have the following comments:
- Why do you propose a frequency domain algorithm? The time domain imaging algorithms[1,2] are much more accurate. Please emphasize the significance of the proposed approach in comparison with the time domain imaging algorithms. It’s also beneficial to compare the imaging performance of the proposed approach with the time-domain imaging algorithm.
- Motion error is a key problem in the practical application of SAR image formulation[3]? Is it possible for the proposed approach to be integrated with some autofocus algorithms? Adding some experiments is intractable, however, some discussion is beneficial.
- A language revision is necessary to fit some minor grammatical errors, e.g. the misuse of prepositions affects the readability of article.
- The authors should pay attention to the format of references.
[1] G. Xu, S. Zhou, L. Yang, S. Deng, Y. Wang and M. Xing, "Efficient Fast Time-Domain Processing Framework for Airborne Bistatic SAR Continuous Imaging Integrated With Data-Driven Motion Compensation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022
[2] H. Xie et al., "Fast Factorized Backprojection Algorithm for One-Stationary Bistatic Spotlight Circular SAR Image Formation," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 4, pp. 1494-1510, April 2017
[3] W. Pu, "SAE-Net: A Deep Neural Network for SAR Autofocus," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3139914.
Author Response
Please see the attachment
Author Response File: Author Response.pdf
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
