Efficient Attributed Scattering Center Extraction Using Gradient-Based Optimization for SAR Image

Jiawei Luan

doi:10.1109/LAWP.2025.3599562

Efficient Attributed Scattering Center Extraction Using Gradient-Based Optimization for SAR Image

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DOI：10.1109/LAWP.2025.3599562

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The attributed scattering center (ASC) model provides a concise representation of high-frequency electromagnetic scattering of targets, making it widely applicable to synthetic aperture radar target characteristic analysis and recognition. However, ASC extraction, as a nonconvex parameter estimation problem, is computationally time-consuming in global optimization due to the nonconvexity of loss function. While gradient-based optimization methods in deep learning frameworks can significantly improve efficiency, applying them directly to nonconvex optimization remains challenging. To solve this issue, this letter proposes an efficient gradient-based ASC extraction method. First, a more convex-like loss function is designed using windowing techniques to broaden the convergence region and reduce the difficulty of nonconvex optimization. Then, an image-frequency domain joint parameter initialization and estimation strategy is developed, achieving fully gradient-based parameter estimation. Validation on simulated and measured data confirms the effectiveness of the proposed method. While maintaining accuracy, it improves ASC extraction efficiency for tank targets from a minute-level to a second-level.

Keywords:

Attributed scattering center (ASC)

gradient-based optimization

synthetic aperture radar (SAR)

Efficient Attributed Scattering Center Extraction Using Gradient-Based Optimization for SAR Image

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