小样本条件下雷达信号的生成与轻量化识别OA北大核心CSTPCD

Objectives In order to study the problems that the current deep learning method requires massive data,complex network,large amount of computation,and high equipment requirements in radar signal recog-nition,Methods a radar signal generation and recognition algorithm jointly improving CycleGAN and Mo-bileNetV3-Small was proposed.Firstly,eight common radar signal types were selected and combined to con-struct the time-domain sequence.In order to better preserve the time-frequency features,the image dataset was formed through the Choi-Williams distribution in the signal pre-processing stage.In the dataset expan-sion stage,the image dataset was used as the input of the CycleGAN migration network and constrained to guide the generation of target images to solve the problem of insufficient samples.Secondly,the U-Net struc-ture and residual dense blocks were introduced into the generator of CycleGAN and the discriminator dis-criminant and loss function were changed to solve the problems of feature blurring and gradient disappear-ance during the dataset augmentation.Finally,in the signal recognition stage,a representative MobileNetV3-Small lightweight network was constructed to complete the recognition verification task.Results The image evaluation index of CycleGAN of the image generation network was 39.74 dB for PSNR and 0.95 for SSIM,the number of parameters for 100 iteration training of the MobileNet-Small signal recognition network model was 1 538 942,and the total running time was 2 152 s.The FLOPs was 127 351 188,and the accuracy rate was 99.30%.Conclusions The image generated by the proposed algorithm had high similarity and small dis-tortion with the real sample,and the recognition speed was greatly improved without sacrificing the accuracy rate,which effectively realized the high accuracy recognition of radar signals under small sample conditions.