基于自监督复数域深度学习网络的 SAR 有源压制干扰抑制方法

Synthetic aperture radar (SAR) has the advantages of all-day, all-weather, and high-resolution in the earth observation, but it is susceptible to electromagnetic interference during the imaging process, which seriously affects the sub⁃ sequent interpretation of SAR images. To this end, this paper proposes a suppression method for SAR blanketing jamming based on self-supervised complex-valued deep learning, and proposes a novel complex-valued interference suppression net⁃ work, which can make full use of the amplitude and phase information of SAR complex images. The weights, activation functions and convolution operations of the network are designed for complex domain processing, and the different informa⁃ tion representations of target and clutter in amplitude and phase in SAR images are mined to achieve interference suppres⁃ sion. Meanwhile, a self-supervised training strategy is proposed to solve the problem of relying heavily on manually labeled samples in the traditional network training process, and is suitable for application scenarios where samples are difficult to be labeled under complex interference. The simulation analysis and experimental verification are carried out. The experimental results show that the proposed method can effectively suppress the active jamming of complex backgrounds, and has the ability of self-supervised intelligent interference suppression.