Inverse Synthetic Aperture Radar, Radar Cross Section, and Iterative Smooth Reweighting $\ell_1$-minimization

TL;DR

This paper compares backprojection and iterative smooth reweighted ℓ₁-minimization methods for analyzing Radar Cross Section data, highlighting their respective robustness, accuracy, and resolution limitations in extracting RCS from measured objects.

Contribution

It introduces a comparative analysis of two RCS extraction methods, emphasizing the advantages and limitations of each in terms of robustness, accuracy, and resolution.

Findings

Backprojection is robust and accurate but resolution-limited.

Iterative smooth reweighted ℓ₁-minimization can resolve closely spaced scatterers.

Backprojection outperforms in robustness, while ℓ₁-minimization offers higher resolution.

Abstract

Radar Cross Section measurement data is often analyzed using Inverse Synthetic Aperture Radar images. This paper compares backprojection and iterative smooth reweighted $\ell_1$-minimization as methods to analyze radar cross section measurements and extract radar cross section for parts of the measured object. The main conclusion is that using backprojection images to extract RCS is robust and accurate but is more limited by the resolution than iterative smooth reweighted $\ell_1$-minimization. The latter method can be used for closely spaced scatterers but is limited in accuracy.