使用级联块行的低秩压缩加速面向百万箔条的RCS计算的S-NFC算法
Accelerated S-NFC for Million-Chaff RCS Computation Using Low-Rank Compression of Concatenated Block Rows
AI总结：
本文提出级联块行低秩压缩的S-NFC算法，实现百万箔条RCS计算6.92倍端到端加速，仅存储6.60%保留耦合，复远场误差0.253%，适用于大规模箔条云RCS分析。
AI中文摘要：
通过忽略远场耦合的稀疏化（S-NFC）算法，仅保留重要的局部电磁相互作用，可实现对大规模箔条云的快速全波雷达截面积（RCS）分析。本文进一步加速S-NFC算法，将与每个接收箔条单元相关的保留非对角相互作用块进行级联，并应用具有共享接收侧基的联合低秩分解。算法保留精确的自相互作用，重复的箔条模板复用预计算的自相互作用块的LU分解。该压缩公式减少了保留耦合的存储量和矩阵-向量乘法成本，同时降低了广义共轭残差求解器所需的迭代次数。针对100000个箔条单元的数值测试表明，在稀疏区域，低秩近似的复远场误差低于1%，并确定了在足够大的平均间距下的实际仅自作用极限。对于百万箔条羽流，所提出的压缩S-NFC算法实现了比未压缩S-NFC高6.92倍的端到端加速，仅存储6.60%的保留耦合，复远场误差为0.253%。
英文摘要：
Sparsification via neglecting far-field coupling (S-NFC) enables fast full-wave radar-cross-section analysis of large-scale chaff clouds by retaining only significant local electromagnetic interactions. This letter further accelerates S-NFC by concatenating the retained off-diagonal interaction blocks associated with each receiving chaff element and applying a joint low-rank factorization with a shared receiving-side basis. Exact self interactions are preserved, while repeated chaff templates reuse precomputed lower--upper factorizations of the self-interaction blocks. The compressed formulation reduces retained-coupling storage and matrix--vector multiplication cost and also decreases the number of iterations required by the generalized conjugate residual solver. Numerical tests with 100,000 chaff elements demonstrate sub-$1\%$ complex-far-field error for low-rank approximations in sparse regimes and identify a practical self-only limit at sufficiently large mean spacing. For a one-million-chaff plume, the proposed compressed S-NFC achieves a $6.92\times$ end-to-end speedup over uncompressed S-NFC while storing only $6.60\%$ of the retained coupling, with a complex-far-field error of $0.253\%$.
