SA-ISAR Imaging via Detail Enhancement Operator and Adaptive Threshold Sensing

Sparse aperture inverse synthetic aperture radar (SA-ISAR) aims to reconstruct target images by undersampled data. Traditional algorithms are limited in their application scope and exhibit weak capabilities in reconstructing target details. To address these issues, an adaptive threshold sensing (ATS) sparse reconstruction algorithm based on alternating direction method of multiplier (ADMM), named ATS-ADMM, is proposed. Within our framework, a detail-enhancing operator (DEO) is designed and combined with the $l_{1}$ -norm to form a joint constrained optimization function to facilitate the recovery of weak scatterers. The matrix inversion operation within the ADMM framework is optimized to efficiently solve the multiconstrained problem. To enhance clutter suppression, an adaptive threshold network is designed based on deep convolutional networks. Inspired by deep learning, the DEO is set as a learnable operator, and the parameters are trained using an unsupervised network. Finally, the performance of ATS-ADMM is validated by comparing it with advanced algorithms using both simulated and real data. The results demonstrate that ATS-ADMM effectively focuses images, is suitable for diverse imaging scenarios, and is the fastest among ADMM-based algorithms.