用频域降维加速电磁逆散射，实现亚秒级成像。
Fast Physics-Driven Untrained Network for Highly Nonlinear Inverse Scattering Problems
- 将电流展开为截断傅里叶基，降低优化维度至低频参数空间。
- 在噪声和天线不确定性下仍保持稳定，速度比现有方法快100倍。
- 适合需要实时成像的微波探测场景，如安检、无损检测。
无训练神经网络（UNNs）虽能高保真重建电磁逆散射问题，但受限于高维空间优化的计算开销。本文提出一种实时物理驱动傅里叶谱（PDF）求解器，通过将感应电流用截断傅里叶基展开，将优化限制在由散射测量支持的紧凑低频参数空间中，实现亚秒级重建。求解器结合收缩积分方程（CIE）缓解高对比度非线性，并引入对比度补偿算子（CCO）校正谱域衰减。此外，设计桥接抑制损失以增强相邻散射体间的边界清晰度。数值与实验结果表明，该方法相比当前最优UNNs提速100倍，且在噪声和天线不确定性下表现鲁棒，适用于实时微波成像应用。
原文摘要 · Abstract (English)
Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction but are computationally limited by high-dimensional spatial-domain optimization. We propose a Real-Time Physics-Driven Fourier-Spectral (PDF) solver that achieves sub-second reconstruction through spectral-domain dimensionality reduction. By expanding induced currents using a truncated Fourier basis, the optimization is confined to a compact low-frequency parameter space supported by scattering measurements. The solver integrates a contraction integral equation (CIE) to mitigate high-contrast nonlinearity and a contrast-compensated operator (CCO) to correct spectral-induced attenuation. Furthermore, a bridge-suppressing loss is formulated to enhance boundary sharpness between adjacent scatterers. Numerical and experimental results demonstrate a 100-fold speedup over state-of-the-art UNNs with robust performance under noise and antenna uncertainties, enabling real-time microwave imaging applications.
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