## 1 Introduction  
In recent years, with the great progress of modern underwater acoustic signal processing technology and underwater acoustic equipment research and development technology, underwater imaging sonar simulation technology has become a research hotspot at home and abroad. Sonar simulation technology plays an important role in both military and civil fields. Especially in the military field, the detection and recognition of enemy military targets (submarines, torpedoes, dangerous obstacles), seafloor terrain matching navigation, and other fields urgently require the application of high-fidelity sonar image simulation technology in complex environments. With the continuous enhancement of computers' computing performance, sonar simulation technology also needs to be developed in the direction of intelligence. The data generated by computer sonar simulations can be used in image processing, underwater target detection, performance monitoring, fault detection, and other fields, which can greatly reduce the dependence of operators on sonar imaging hardware devices. At present, research using neural networks has made remarkable progress, and they can be combined with computer technology and signal processing technology to make sonar data simulation intelligent. The traditional image simulation technology requires operators to have a high level of theoretical knowledge [1; 2; 3; 4]. The simulation model needs to consider the contradiction between the simulation accuracy and simulation calculation efficiency in complex environments, which leads to the incomprehensive distribution of sonar images and low intelligence of the model. With the rapid improvement in computer hardware computing ability and the large-scale application of supercomputers, deep learning methods have developed rapidly, and have been applied to computer vision tasks in the fields of medicine, radar, sonar, etc. For example, convolutional neural network (CNN) has been applied to the analysis and feature extraction of static medical images and radar images, and has achieved good results. Since [PERSON] [5] proposed the GAN method in 2014, it has become a hot research object in various fields. In recent years, generative adversarial networks (GANs) have been used for noise reduction and image simulation in medical imaging [6; 7; 8]. In the field of radar, an image-to-image conversion technology using a GAN [9; 10] has also been proposed, which can successfully transform low-resolution SAR imaging to high-resolution SAR imaging. In the sonar field, GANs have been applied to the underwater sonar image simulation task [11; 12], but the network structure of these methods does not fuse the category condition information of the sonar image, and ignores the correlation between pixels and channels, resulting in poor controllability and low fidelity of the generated sonar images. Based on the above analysis, sonar data generation faces significant challenges: The noise in sonar images is complex, the target signal is weak, and it is difficult to generate high-fidelity data without losing the target features. We pay more attention to the sonar target pixels in the image than other pixels in the image background. The convolution operation is limited by the receptive field, and it cannot extract the structural features of the image well [13; 14]. Attention mechanisms can better calculate the degree of correlation between different positions, where a pixel-level attention mechanism calculates the proportion of each pixel point in all pixel points through the softmax operation, and then obtains the pixel weight matrix. The channel-level attention mechanism calculates the ratio of each channel to all channels in the feature map by using the softmax operation to obtain the channel weight matrix.