Through-the-Wall Nonlinear SAR Imaging
Michael V. Klibanov, Alexey V. Smirnov, Vo Anh Khoa, Anders J., Sullivan, Lam H. Nguyen

TL;DR
This paper presents a fully nonlinear SAR imaging method for through-the-wall scenarios using a convexification inversion scheme, achieving higher accuracy than traditional linearized models in both simulated and real data.
Contribution
It introduces a globally convergent nonlinear inversion approach for through-the-wall SAR imaging that overcomes local minima issues inherent in linearized models.
Findings
The proposed method accurately estimates target dielectric properties behind walls.
Numerical results outperform traditional Born approximation-based methods.
The approach is validated on both simulated and experimental data.
Abstract
An inverse scattering problem for SAR data in application to through-the-wall imaging is addressed. In contrast with the conventional algorithms of SAR imaging, that work with the linearized mathematical model based on the Born approximation, the fully nonlinear case is considered here. To avoid the local minima problem, the so-called "convexification" globally convergent inversion scheme is applied to approximate the distribution of the slant range (SR) dielectric constant in the 3-D domain. The benchmark scene of this paper comprises a homogeneous dielectric wall and different dielectric targets hidden behind it. The results comprise two dimensional images of the SR dielectric constant of the scene of interest. Numerical results are obtained by the proposed inversion method for both the computationally simulated and experimental data. Our results show that the values, cross-range…
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