An End-to-End Multidomain Interaction Deep Unrolling Network Based on Block-Aware Optimization Model for ISAR Multitarget Separation

In complex maritime scenarios, multiple targets in the same radar beam often degrade the quality of inverse synthetic aperture radar (ISAR) imaging. Most existing methods typically leverage a single domain for target separation, hardly considering the relationships across multiple domains. To fully exploit the interdomain interactions, we propose an end-to-end multidomain interaction deep unrolling network based on a block-aware optimization model, termed MDIB-Net, to simultaneously achieve multitarget separation and echo reconstruction. Combining target structural features and deep unrolling techniques, this model-driven network effectively explores the correlations of the same target across different domains to achieve target separation. The MDIB-Net comprises cascaded iteration blocks, where each iteration block consists of an optimization block and a domain interaction (DIR) block. The optimization block solves the proposed block-aware multitarget separation function, leveraging low-rank properties, local similarity, and structural priors to capture the physical characteristics of targets. Moreover, a learnable module is incorporated in this block to discover the optimal transformation domain, thereby enhancing the structural prior of targets. The DIR block introduces a lightweight module to extract the semantic maps from the high-resolution range profiles (HRRP) domain. The DIR block further incorporates a spatial-adaptive semantic guidance module, which takes the semantic maps as guidance, to refine the transformation domain features, effectively promoting cross-domain feature interactions. Additionally, the MDIB-Net achieves multitarget separation by dynamically adjusting its iterative strategy according to the predefined target number, demonstrating both robustness and flexibility. Simulated and measured experiments validate the effectiveness of the proposed method.