Monitoring the infrared video of the unmanned aerial vehicle (UAV) group is a new hot spot in the security and military fields. Due to the difficulty of acquiring UAV images in complex backgrounds, and the number of images cannot meet the requirements of model training and verification of related algorithms, an image simulation method of infrared UAV based on image derivation is proposed. This method is used to simulate infrared UAVs. This method is used to mix the infrared UAV template image and the infrared background image to generate a large number of UAV target images in different backgrounds. Aiming at the problems of image mixing technology being severely affected by background noise, blurring of drone target edges, and low harmony of synthetic images, an unsupervised generative confrontation network is used to generate a gray-scale constrained image with a high degree of harmony. The target gradient image is used as a joint constraint to solve the Gaussian-Poisson equation, and a mixed image with high consistency with the real image characteristics is obtained. The experimental results show that the mixed image generated by the proposed method has high image harmony and visual authenticity, which shows that the obtained image as an extended sample can effectively improve the performance of the machine learning algorithm.