Automatic Modulation Recognition of Dual-Component Radar Signals Using ResSwinT-SwinT Network
Bing Ren, Kah Chan Teh, Hongyang An, Erry Gunawan
Abstract
Automatic modulation recognition plays an important role in military and civilian applications, identifying the modulation format of received signals before signal demodulation. With the increasing complexity and density of the electromagnetic environment, the multi-component modulation radar signal recognition against various signal-to-noise ratio (SNR) conditions has become a practical and urgent problem. In this paper, we propose a dual-component modulation recognition framework, which incorporates the residual Swin transformer denoise network (ResSwinT), Swin transformer feature extraction network (SwinT), residual-attention (RA) modulation recognition head, and SNR level classifier and achieves robust recognition performance against various SNR conditions with tolerable complexity and accuracy trade-off. Firstly, the time-frequency analysis is employed to transform dual-component radar signals into time-frequency images (TFIs). Then, the TFIs at various SNR levels are applied to the SwinT, which generates shallow and deep feature representations for the SNR classifier and RA-modulation recognition head, respectively. The ResSwinT is initiated to reconstruct low SNR TFIs only, which are again processed by the SwinT. Finally, the RA-modulation recognition head provides modulation format predictions. The proposed framework can identify randomly combined dual-component radar signals from 12 modulation formats, meanwhile, improving the utilization of the SwinT feature and reducing unnecessary computation of the ResSwinT. Simulation results show that the proposed scheme can obtain an exact match ratio (EMR) of larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {97\%}$</tex-math></inline-formula> at SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {>\!-6}$</tex-math></inline-formula> dB. At low SNR condition ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {-12}$</tex-math></inline-formula> dB), the ResSwinT can obtain about EMR gain of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {20\%}$</tex-math></inline-formula> and the overall framework can achieve EMR of more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {80\%}$</tex-math></inline-formula> , which outperforms other state-of-the-art methods and obtains better generalization capability.