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
Traditional international trade theories seldom consider the pursuit of power as maintaining and enlarging a country's relative strength in the process of interstate competition.However,in the context of great pow...Traditional international trade theories seldom consider the pursuit of power as maintaining and enlarging a country's relative strength in the process of interstate competition.However,in the context of great power competition,the importance of power in the trade between advanced and latecomer countries has become more pronounced.Given this situation,we can establish a tractable and generalized international economic political framework for analyzing great power competition by introducing the power factor into classic two-country trade theory,treating the absolute welfare and relative welfare emphasized by economics and political science respectively as actors'dual objectives,and making use of game theory methods.It can be found that in a two-country game composed of an advanced and a latecomer country,the latter will prefer a strategy of"enhancing its own strength and rectifying its own weak points,"while the former will favor a strategy that""worsens the other's weak points and enhances the other's strengths."Once the technological gap that determines comparative advantage narrows,the advanced country will tend to totally suppress the latecomer and even"decouple"from it.展开更多
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.
文摘Traditional international trade theories seldom consider the pursuit of power as maintaining and enlarging a country's relative strength in the process of interstate competition.However,in the context of great power competition,the importance of power in the trade between advanced and latecomer countries has become more pronounced.Given this situation,we can establish a tractable and generalized international economic political framework for analyzing great power competition by introducing the power factor into classic two-country trade theory,treating the absolute welfare and relative welfare emphasized by economics and political science respectively as actors'dual objectives,and making use of game theory methods.It can be found that in a two-country game composed of an advanced and a latecomer country,the latter will prefer a strategy of"enhancing its own strength and rectifying its own weak points,"while the former will favor a strategy that""worsens the other's weak points and enhances the other's strengths."Once the technological gap that determines comparative advantage narrows,the advanced country will tend to totally suppress the latecomer and even"decouple"from it.
