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
Addressing the issue of low pulse identification rates for low probability of intercept(LPI)radar signals under low signal-to-noise ratio(SNR)conditions,this paper aims to investigate a new method in the field of deep...Addressing the issue of low pulse identification rates for low probability of intercept(LPI)radar signals under low signal-to-noise ratio(SNR)conditions,this paper aims to investigate a new method in the field of deep learning to recognize modulation types of LPI radar signals efficiently.A novel algorithm combining dual efficient network(DEN)and non-local means(NLM)denoising was proposed for the identification and selection of LPI radar signals.Time-domain signals for 12 radar modulation types were simulated,adding Gaussian white noise at various SNRs to replicate complex electronic countermeasure scenarios.On this basis,the noisy radar signals undergo Choi-Williams distribution(CWD)time-frequency transformation,converting the signals into two-dimensional(2D)time-frequency images(TFIs).The TFIs are then denoised using the NLM algorithm.Finally,the denoised data is fed into the designed DEN for training and testing,with the selection results output through a softmax classifier.Simulation results demonstrate that at an SNR of-8 dB,the algorithm can achieve a recognition accuracy of 97.22%for LPI radar signals,exhibiting excellent performance under low SNR conditions.Comparative demonstrations prove that the DEN has good robustness and generalization performance under conditions of small sample sizes.This research provides a novel and effective solution for further improving the accuracy of identification and selection of LPI radar signals.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%.
文摘Addressing the issue of low pulse identification rates for low probability of intercept(LPI)radar signals under low signal-to-noise ratio(SNR)conditions,this paper aims to investigate a new method in the field of deep learning to recognize modulation types of LPI radar signals efficiently.A novel algorithm combining dual efficient network(DEN)and non-local means(NLM)denoising was proposed for the identification and selection of LPI radar signals.Time-domain signals for 12 radar modulation types were simulated,adding Gaussian white noise at various SNRs to replicate complex electronic countermeasure scenarios.On this basis,the noisy radar signals undergo Choi-Williams distribution(CWD)time-frequency transformation,converting the signals into two-dimensional(2D)time-frequency images(TFIs).The TFIs are then denoised using the NLM algorithm.Finally,the denoised data is fed into the designed DEN for training and testing,with the selection results output through a softmax classifier.Simulation results demonstrate that at an SNR of-8 dB,the algorithm can achieve a recognition accuracy of 97.22%for LPI radar signals,exhibiting excellent performance under low SNR conditions.Comparative demonstrations prove that the DEN has good robustness and generalization performance under conditions of small sample sizes.This research provides a novel and effective solution for further improving the accuracy of identification and selection of LPI radar signals.
