kumoh national institute of technology
Networked Systems Lab.

Rubina Akter, Van-Sang Doan, Thien Huynh-The, and Dong-Seong Kim, RFDOA-Net: An Efficient ConvNet for RF-based DOA Estimation in UAV Surveillance Systems, IEEE Transactions on Vehicular Technology, vol.7, pp. 12209-12214, November 2021. DOI: 10.1109/TVT.2021.3114058
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Date : 2021-11-22
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Title: RFDOA-Net: Residual CNN Based DOA Estimation of RF Signal for UAV Surveillance System.
Journal Name: IEEE Transactions on Vehicular Technology

This paper presents a convolution neural network (CNN)-based direction of arrival (DOA) estimation method for radio frequency (RF) signals acquired by a nonuniform linear antenna array (NULA) in unmanned aerial vehicle (UAV) localization systems. The proposed deep CNN, namely RFDOA-Net, is designed with three primary processing modules, such as collective feature extraction, multi-scaling feature processing, and complexity-accuracy trade-off, to learn the multi-scale intrinsic characteristics for multi-class angle classification. In several specific modules, the regular convolutional and grouped convolutional layers are leveraged with different filter sizes to
enrich diversified features and reduce network complexity besides adopting residual connection to prevent vanishing gradient. For
performance evaluation, we generate a synthetic signal dataset for DOA estimation under the multipath propagation channel with the presence of additive noise, propagation attenuation and delay. In simulations, the effectiveness of RFDOA-Net is investigated comprehensively with various processing modules and antenna configurations. Compared with several state-of-the-art deep learning-based models, RFDOA-Net shows the superiority in terms of accuracy with over 94% accuracy at 5 dB signal-to noise ratio (SNR) with cost-efficiency.

Paper Submission Date: 29 September 2020
Paper Acceptance Date: 9 September 2021

DOI: 10.1109/TVT.2021.3114058