With the rapid development of communication technology, due to the advantages of high flexibility, good compatibility, wide openness and other advantages of software radio, and it is easy to upgrade and expand the system in the later stage, software radio technology has been applied in many radio engineering occasions, showing broad application prospects. This paper applies the GNU Radio experimental platform, and selects eight modulation methods of AM, DSB, SSB, 2ASK, MASK, 2FSK, 2PSK, MPSK, etc., through the setting of basic parameters such as frequency, phase, FFT points, window function and the setting of channel parameters such as additive white Gaussian noise, frequency offset, epsilon and signal-to-noise ratio range, to obtain different simulated time domain charts, frequency domain charts, waterfall charts, constellation charts, and compare and analyze different signal modulation techniques, understand and master the modulation principles and modulation methods of signals. At the same time, modeled on a number of open source datasets such as RML2016.10A and RML2018.01A, a small sample electromagnetic dataset is formed in the GNU Radio software platform, providing a reliable data set for technical research in the field of electromagnetic space and providing more general options for researchers who design related systems.

In distance-selected imaging, the contrast of laser images is reduced due to long imaging distances, insufficient laser power, and atmospheric turbulence. An enhancement algorithm based on the EnlightenGAN network is proposed to improve the contrast of laser images. Firstly, the laser images are acquired using a distance selection pass system to establish the laser image dataset and expand the dataset, and the traditional algorithm is used to enhance the images and establish the mapping relationship between low-quality images and high-quality images. The global discriminator based on PatchGAN with the improved VGG model is used to regularize the self-feature retention loss and construct the depth link between the global discriminator and the local discriminator to improve the generalization ability of the model; adjust the attention map to the second layer before the CLB convolution module and also add the residual structure in the second layer CLB to improve the robustness of the model; adopt the idea of gray-scale layering with a low drop and high rise to improve the self regularization mechanism to achieve the enhancement of the key region; finally, use the improved EnlightenGAN to fit the relationship between a low-quality image and high-quality image. Finally, EnlightenGAN is used to fit the relationship between low-quality images and high-quality images, extract laser image features, and enhance low-quality images. The experimental results show that the improved algorithm improves PSNR by 12.3% and 0.7% on average, SSIM by 57% and 10.3% on average, and NIQE by 21% and 13% on average compared to other algorithms and the original EnlightenGAN algorithm, respectively. The algorithm improves the signal-to-noise ratio and contrast of laser images with richer image details. It provides a new idea for pre-processing laser images.