Lee Jeong-Wan, Jung Ye Chan, Yang Sung-Jun
Department of Electronic Engineering, Seoul National University of Science and Technology (Seoultech), Seoul 01811, Republic of Korea.
Sensors (Basel). 2025 Aug 2;25(15):4771. doi: 10.3390/s25154771.
This communication presents an accurate and computationally efficient approach for wideband radar cross-section (RCS) estimation and scattering point reconstruction using infinitesimal dipole modeling (IDM) with compressive sensing. The proposed method eliminates the need for field sampling at numerous frequency points across the wideband range through Green's function adjustment. Additionally, compressive sensing is employed for induced current calculation to reduce both frequency and angular sampling requirements. Numerical validation demonstrates that the method achieves a 50% reduction in field sample data and an 82.3% reduction in IDM processing time while maintaining comparable accuracy through Green's function adjustment. Furthermore, compared to approaches without compressive sensing, the method shows a 55.1% and a 75.5% reduction in error in averaged RCS for VV-pol and HH-pol, respectively. The proposed method facilitates efficient wideband RCS estimation of various targets while significantly reducing measurement complexity and computational cost.
本文介绍了一种精确且计算高效的方法，用于基于带有压缩感知的无限小偶极子建模（IDM）进行宽带雷达散射截面（RCS）估计和散射点重建。所提出的方法通过格林函数调整，无需在宽带范围内的众多频率点进行场采样。此外，采用压缩感知进行感应电流计算，以减少频率和角度采样需求。数值验证表明，该方法通过格林函数调整，在保持可比精度的同时，实现了场采样数据减少50%以及IDM处理时间减少82.3%。此外，与无压缩感知的方法相比，该方法在垂直极化（VV-pol）和水平极化（HH-pol）的平均RCS误差分别降低了55.1%和75.5%。所提出的方法有助于高效地对各种目标进行宽带RCS估计，同时显著降低测量复杂度和计算成本。
