  Most existing sparse representation-based approaches for attributed
scattering center (ASC) extraction adopt traditional iterative optimization
algorithms, which suffer from lengthy computation times and limited precision.
This paper presents a solution by introducing an interpretable network that can
effectively and rapidly extract ASC via deep unfolding. Initially, we create a
dictionary containing reliable prior knowledge and apply it to the iterative
shrinkage-thresholding algorithm (ISTA). Then, we unfold ISTA into a neural
network, employing it to autonomously and precisely optimize the
hyperparameters. The interpretability of physics is retained by applying a
dictionary with physical meaning. The experiments are conducted on multiple
test sets with diverse data distributions and demonstrate the superior
performance and generalizability of our method.
