Compressive Inverse Scattering II. SISO Measurements with Born scatterers
Albert C. Fannjiang

TL;DR
This paper introduces compressive inverse scattering methods using SISO measurements for low-sparsity scatterers, employing novel sampling schemes and basis transformations to enable accurate reconstruction of both point and extended targets.
Contribution
It proposes new sampling techniques and analysis frameworks, including the use of Littlewood-Paley basis, for efficient compressive imaging in inverse scattering with SISO data.
Findings
Exact recovery of low-sparsity scatterers demonstrated
Sampling schemes transform scattering matrices into random Fourier matrices
Scale-by-scale reconstruction of extended targets achieved
Abstract
Inverse scattering methods capable of compressive imaging are proposed and analyzed. The methods employ randomly and repeatedly (multiple-shot) the single-input-single-output (SISO) measurements in which the probe frequencies, the incident and the sampling directions are related in a precise way and are capable of recovering exactly scatterers of sufficiently low sparsity. For point targets, various sampling techniques are proposed to transform the scattering matrix into the random Fourier matrix. The results for point targets are then extended to the case of localized extended targets by interpolating from grid points. In particular, an explicit error bound is derived for the piece-wise constant interpolation which is shown to be a practical way of discretizing localized extended targets and enabling the compressed sensing techniques. For distributed extended targets, the…
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