High-Fidelity SAR Imaging Under Composite Jamming via Collaborative Sparse Signal Optimization

In practical combat scenarios, the high-resolution imaging process of synthetic aperture radar (SAR) is susceptible to diverse forms electromagnetic jamming, which severely degrades the final imaging quality. To achieve high-fidelity SAR imaging under electronic countermeasure environments, this paper proposes a collaborative sparse signal optimization framework to reconstruct the SAR signals. By exploiting the sparsity of SAR signals and composite jamming signals in distinct domains, dictionaries are constructed separately for each, thereby establishing a collaborative sparse optimization problem. Furthermore, an inertial symmetric regularized alternating direction method of multipliers (ISRADMM) is developed to solve the formulated optimization problem through iterative computation. The proposed method achieves efficient and high-fidelity SAR imaging in composite jamming scenarios, thereby significantly enhancing subsequent target discrimination capability, as validated by entropy-based metrics. Comprehensive validation through numerical studies, simulations, and measured data experiments demonstrates the superior signal recovery and high-fidelity SAR imaging capability of this methodology under composite jamming scenarios.