Neural network-based methods for intrapulse modulation recognition in radar signals have demonstrated significant improvements in classification accuracy.However,these approaches often rely on complex network structur...Neural network-based methods for intrapulse modulation recognition in radar signals have demonstrated significant improvements in classification accuracy.However,these approaches often rely on complex network structures,resulting in high computational resource requirements that limit their practical deployment in real-world settings.To address this issue,this paper proposes a bottleneck residual network with efficient soft-thresholding(BRN-EST)network,which integrates multiple lightweight design strategies and noise-reduction modules to maintain high recognition accuracy while significantly reducing computational complexity.Experimental results on the classical low-probability-of-intercept(LPI)radar signal dataset demonstrate that BRN-EST achieves comparable accuracy to state-of-the-art methods while reducing computational complexity by approximately 50%.展开更多
基金supported by the National Defense Pre-Research Foundation of China during the“14th Five-Year Plan”under Grant No.629010204.
文摘Neural network-based methods for intrapulse modulation recognition in radar signals have demonstrated significant improvements in classification accuracy.However,these approaches often rely on complex network structures,resulting in high computational resource requirements that limit their practical deployment in real-world settings.To address this issue,this paper proposes a bottleneck residual network with efficient soft-thresholding(BRN-EST)network,which integrates multiple lightweight design strategies and noise-reduction modules to maintain high recognition accuracy while significantly reducing computational complexity.Experimental results on the classical low-probability-of-intercept(LPI)radar signal dataset demonstrate that BRN-EST achieves comparable accuracy to state-of-the-art methods while reducing computational complexity by approximately 50%.
