Deep Unfolding for Sparse Aperture ISAR Imaging via Compressive Sensing: BLCAN

The challenge of inverse synthetic aperture radar (ISAR) imaging under sparse aperture (SA) conditions using compressed sensing (CS) techniques has attracted significant attention. Current CS-based methods suffer from feature information degradation during iterative reconstruction, leading to sparse images lacking detailed structural characteristics. Inspired by the memory storage mechanisms of the human brain, this paper proposes a novel framework for sparse aperture ISAR imaging. The framework integrates brain-inspired cognitive proximal mapping modules (BLCPMM) within the alternating direction method of multipliers (ADMM) algorithm. To address information degradation between stages, a brain-like cognitive transitional memory (BLCTM) module is introduced. Additionally, a brain-like cognitive global memory (BLCGM) module is incorporated to ensure information continuity across stages. The proposed model, termed the Brain-Like Cognitive ADMM Network (BLCAN), effectively mitigates feature memory loss during iterative transmissions. Simulation and experimental results demonstrate that BLCAN significantly improves image quality under challenging conditions, exhibiting superior robustness and adaptability.