发表机构
Northwestern Polytechnical University（西北工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CRPDNN，无需初步重建，三维电磁逆散射中误差低、速度快，稳定性好且适用于噪声场景，具实际应用潜力。
AI 中文摘要
电磁逆散射是一个非线性且不适定的问题，由于测量限制、噪声和高计算成本，准确重建极具挑战性，尤其是三维成像场景。尽管物理驱动神经网络（PDNN）减少了对带标签训练数据的依赖，但现有的加速PDNN框架通常依赖基于初步重建的区域选择，当所选区域不准确时可能会引入不稳定性。本文提出了一种用于三维电磁逆散射的坐标残差物理驱动神经网络（CRPDNN），该求解器使用归一化空间坐标和残差卷积网络直接重建未知对比度分布，无需初步重建。在报道的无噪声三维合成案例中，CRPDNN的平均相对误差为2.10%，而CSI方法为7.97%、$L_{2/3}$-FBE-WCIE方法为3.99%，同时分别比这两个基准方法快约5.5倍和12.1倍。补充的二维比较进一步证实，与现有PDNN框架相比，CRPDNN具有更好的稳定性和计算效率，在有噪声测量下也能保持可靠的重建性能，三维菲涅耳实验进一步表明其在实际成像应用中的潜力，相关代码可在指定URL获取。
英文摘要
Electromagnetic inverse scattering is a nonlinear and ill-posed computational imaging problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging. Although physics-driven neural networks (PDNNs) reduce the dependence on labeled training data, existing accelerated PDNN frameworks often rely on preliminary reconstruction-based region selection, which may introduce instability when the selected region is inaccurate. In this paper, a coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering. CRPDNN represents the unknown complex contrast distribution using normalized spatial coordinates and a residual convolutional network, whose parameters are optimized by enforcing consistency between the measured and model-predicted scattered fields. Unlike existing subregion-accelerated PDNN approaches, CRPDNN does not require a preliminary reconstruction, thereby avoiding dependence on its accuracy. For the reported noise-free 3-D synthetic cases, CRPDNN achieves an average relative error of 2.10\%, compared with 7.97\% for CSI and 3.99\% for $L_{2/3}$-FBE-WCIE, while providing approximately 5.5- and 12.1-fold speedups over the two baselines, respectively. Additional 2-D comparisons further demonstrate its stability and computational efficiency relative to existing PDNN frameworks. CRPDNN also maintains reliable reconstruction performance under noisy measurements, and the 3-D Fresnel experiments further indicate its potential for practical imaging applications.
