# Accurate Wideband RCS Estimation from Limited Field Data Using Infinitesimal Dipole Modeling with Compressive Sensing

## TL;DR

This paper introduces a new method for estimating radar cross-sections using dipole modeling and compressive sensing, reducing data and processing time while keeping accuracy.

## Contribution

The novel approach combines infinitesimal dipole modeling with compressive sensing to reduce sampling requirements for wideband RCS estimation.

## Key findings

- The method reduces field sample data by 50% and processing time by 82.3%.
- Error in averaged RCS is reduced by 55.1% for VV-pol and 75.5% for HH-pol compared to non-compressive methods.

## Abstract

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.