A new regularization method for radar cross section imaging

Radar Cross Section analysis is the study of the scattering behavior of an object. The objective is to determine the main scatterers from measurements of the backscattered electric field. It is generally achieved by Radar Cross Section imaging. This leads to an ill-posed inverse problem because the system to solve is underdetermined. It is then necessary to regularize the problem by adding prior information. In this paper, a new constrained and sparse regularization method for Radar Cross Section imaging is proposed. It is based on the finite spatial electromagnetic extension of the target and its expected small number of scatterers. A least squares criterion with a L1 penalty and support constraints is developed. It is minimized by an efficient algorithm resting on an Alternating Direction Method of Multipliers. The application to real scattered measurements is very promising, with a limited computation time. Compared to the conventional approach, the image resolution is drastically increased.

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