ISAR Super-Resolution and Clutter Suppression Using Deep Learning

Inverse Synthetic Aperture Radar (ISAR) plays a vital role in the high-resolution imaging of marine targets, particularly under non-cooperative scenarios. However, resolution degradation due to limited observation angles and marine clutter such as wave-induced disturbances remains a major challenge. In this work, we propose a novel deep learning-based framework to enhance ISAR resolution in the presence of marine clutter and additive Gaussian noise, which performs direct restoration in the ISAR image domain after an IFFT2 back projection. Under small aspect sweeps with coarse range alignment, the network implicitly compensates for residual defocus and cross-range blur, while suppressing clutter and noise, to recover high-resolution complex ISAR images. Our approach leverages a residual neural network trained to learn a non-linear mapping between low-resolution and high-resolution ISAR images. The network is designed to preserve both magnitude and phase components, thereby maintaining the physical integrity of radar returns. Extensive simulations on synthetic marine vessel data demonstrate significant improvements in cross-range, outperforming conventional sparsity-driven methods. The proposed method also exhibits robust performance under conditions of low signal-to-noise ratio (SNR) and signal-to-wave ratio (SWR), effectively recovering weak scatterers and suppressing false artifacts. This work establishes a promising direction for data-driven ISAR image enhancement in noisy and cluttered maritime environments with minimal pre-processing.