Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

TL;DR

This paper introduces a convex optimization method for ground moving target imaging in SAR that effectively recovers the velocities and locations of scatterers by decomposing a phase-space reflectivity matrix into sparse and rank-one components.

Contribution

The paper formulates GMTI as a phase-space reflectivity recovery problem and develops a computationally efficient convex optimization approach that handles dense targets and clutter without extra suppression techniques.

Findings

Effective recovery of moving targets in dense environments.

Robustness to noise and clutter demonstrated through simulations.

Outperforms state-of-the-art methods in computational efficiency.

Abstract

In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the strengths and velocities of the scatterers in a scene of interest. We show that the discretized PSR matrix can be decomposed into a rank-one, and a highly sparse component corresponding to the stationary and moving scatterers, respectively. We then recover the two distinct components by solving a constrained optimization problem that admits computationally efficient convex solvers within the proximal gradient descent and alternating direction method of multipliers frameworks. Using the structural properties of the PSR matrix, we alleviate the computationally expensive steps associated with rank-constraints, such as singular value thresholding. Our optimization-based approach has several advantages over state-of-the-art GMTI methods, including computational efficiency, applicability to dense target environments, and arbitrary imaging configurations. We present extensive simulations to assess the robustness of our approach to both additive noise and clutter, with increasing number of moving targets. We show that both solvers perform well in dense moving target environments, and low-signal-to-clutter ratios without the need for additional clutter suppression techniques.