kumoh national institute of technology
Networked Systems Lab.

Thien Huynh-The, Cam-Hao Hua, Van-Sang Doan, Quoc-Viet Pham and Dong-Seong Kim,"Accurate Deep CNN-based Waveform Recognition for Intelligent Radar Systems" IEEE Communications Letters, Vol. 25, No. 9, pp. 2938-2942, September 2021, DOI: 10.1109/LCOMM.2021.3095278
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Date : 2021-09-13
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Abstract: Nowadays radar systems have been facing with the disordered electromagnetic spectrum access and utilization in shared spectrum environments with radio communication systems. Numerous waveform recognition methods have been studied with feature engineering and conventional machine learning (ML) for intelligent radar systems, but they are critically challenged by practical problems of scalability and reliability. Deep learning (DL) with the ability to automatically learn the representational features is leveraged to handle the aforementioned obstacles effectively. In this work, we proposed a high-accurate waveform recognition method for intelligent radar systems by developing a novel residual-attention multiscale-accumulation convolutional network (RamNet). By deliberately incorporating the residual connection and attention connection in selective-feature improvement blocks, RamNet can enrich high