kumoh national institute of technology
Networked Systems Lab.

Rubina Akter, Van-Sang Doan, Thien Huynh-The, and Dong-Seong Kim, RFDOA-Net: Residual CNN Based DOA Estimation of RF Signal for UAV Surveillance System, IEEE Signal Processing Letters , 2020. (R)
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Date : 2020-09-11
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r.wollman@ieee.org, rubinaakter2836@kumoh.ac.kr, doansang.g1@gmail.com, thienht@kumoh.ac.kr, dskim@kumoh.ac.kr, arash.mohammadi@concordia.ca, parv.v@lehigh.edu


EIC Immediate Reject -> SPL-29025-2020, RFDOA-Net: Residual CNN Based DOA Estimation of RF Signal for UAV Surveillance System



Dear Prof. Dong Kim,

I am writing to you in regards to manuscript # SPL-29025-2020 entitled "RFDOA-Net: Residual CNN Based DOA Estimation of RF Signal for UAV Surveillance System", which you submitted to the IEEE Signal Processing Letters.

The submitted manuscript was reviewed by our editorial board to determine whether it meets the standards of this journal in terms of scope, presentation and technical depth. Please note that only the submissions which pass the preliminary review are scheduled for a full peer review. Regretfully, the preliminary review concluded that the paper is not suitable for publication in the IEEE Signal Processing Letters. Some specific comments from the editorial board members are below:

Editor 1:
This paper presents a Deep learning based DoA estimation in a UAV surveillance application. The main contribution is the neural network architecture that achieves a desirable performance on the DOA estimation problem and its supporting interpretation. There has been burgeoning growth in the development of Deep learning methods for DoA estimation in the past few years which this paper has not considered. A sample of the recent literature in that regard is here:
1. Li, Qinglong, Xueliang Zhang, and Hao Li. "Online direction of arrival estimation based on deep learning." 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018.
2. Liu, Zhang-Meng, Chenwei Zhang, and S. Yu Philip. "Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections." IEEE Transactions on Antennas and Propagation 66.12 (2018): 7315-7327.
3. Huang, Hongji, et al. "Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system." IEEE Transactions on Vehicular Technology 67.9 (2018): 8549-8560.

It is important to compare the contribution of this work in this scientific context, and identify similar approaches for comparative evaluation. I recommend that the authors resubmit taking this into account.
Editor 2:

I agree with Editor-1's important comment that the paper misses to mention and compare the proposed RFDOA-Net with recent body of work in this context. It is critical to properly place contributions of the paper within the context of recent state-of-the-art methodologies.

Please note that according to IEEE Signal Processing Society policy 6.16 "Handling of Rejected Papers" (https://signalprocessingsociety.org/volunteers/policy-and-procedures-manual), the Society strongly discourages resubmission of rejected manuscripts more than once. Authors should carefully review the aforementioned policy before resubmitting their manuscript.

Thank you for considering the IEEE Signal Processing Letters for the publication of your research. I hope the outcome of this specific submission will not discourage you from the submission of future manuscripts.


Prof. Z. Jane Wang
Editor-in-Chief, IEEE Signal Processing Letters

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