Imaging of moving targets with multi-static SAR using an overcomplete dictionary

TL;DR

This paper introduces a novel multi-static SAR imaging method for moving targets by employing an overcomplete dictionary and compressed sensing, enabling effective linearized inversion in a sparse regularization framework.

Contribution

It presents a new approach that linearizes the moving target imaging problem using an overcomplete dictionary, contrasting with traditional non-linear coupled methods.

Findings

Effective imaging of moving targets with random multi-static configurations.

Linearized inversion improves computational efficiency and robustness.

Utilizes sparsity constraints for accurate target velocity estimation.

Abstract

This paper presents a method for imaging of moving targets using multi-static SAR by treating the problem as one of spatial reflectivity signal inversion over an overcomplete dictionary of target velocities. Since SAR sensor returns can be related to the spatial frequency domain projections of the scattering field, we exploit insights from compressed sensing theory to show that moving targets can be effectively imaged with transmitters and receivers randomly dispersed in a multi-static geometry within a narrow forward cone around the scene of interest. Existing approaches to dealing with moving targets in SAR solve a coupled non-linear problem of target scattering and motion estimation typically through matched filtering. In contrast, by using an overcomplete dictionary approach we effectively linearize the forward model and solve the moving target problem as a larger, unified regularized inversion problem subject to sparsity constraints.