WTE-CGAN Based Signal Enhancement for Weak Target Detection

In this paper, we provide the target signal enhancement method based on deep learning for weak target detection. Firstly, the proposed method fully considers the nature characteristic of radar complex echoes and exploits the complex-valued neural networks. Then, the architecture of weak target enhancement complex-valued generative adversarial network (WTE-CGAN) is proposed. More specifically, the generator loss function of generative adversarial network (GAN) is modified, which can be used to reflect the difference between the generated target signal by the generator and label signal. To keep the training stability of the proposed method, a gradient penalty factor is randomly added to every sample, which embodies the loss function of discriminator. Finally, simulation and measured experiments are given to demonstrate the effectiveness of the proposed method compared with other methods, and it has a significant signal enhancement effect on weak targets.