volume | PIER Journals

Abstract

To address the challenges of high computational complexity in linear system solving and slow convergence of the Alternating Direction Method of Multipliers (ADMM) for compressed sensing Synthetic Aperture Radar (SAR) imaging, this study proposes a precomputation strategy based on Cholesky decomposition. Specifically, the system matrix is decomposed once during the initialization phase and reused across subsequent iterations, substantially reducing the computational overhead associated with the primal variable update. Furthermore, a novel dual-momentum coupling mechanism is designed, and building on Nesterov extrapolation, this mechanism integrates cross-momentum interactions between the real and imaginary components of dual variables, along with the historical variation trends of primal variables, thereby effectively accelerating overall convergence. Both simulated and measured data results demonstrate that the proposed method achieves a significant improvement in computational efficiency while ensuring high imaging quality.

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Dr. Enchen Wang

Dr. Xuechen Zhang

Dr. Daming Lin

Dr. Shumao Qiu