A Multiple Measurement Vector Approach To Synthetic Aperture Radar Imaging

TL;DR

This paper introduces a multiple measurement vector (MMV) approach for synthetic aperture radar (SAR) imaging that leverages polarization and sub-aperture diversity to improve resolution and scene reconstruction.

Contribution

It develops an MMV-based methodology for SAR imaging of scenes with direction-dependent reflectivity, incorporating sparsity and measurement diversity into the resolution analysis.

Findings

Resolution analysis accounts for sparsity, scatterer separation, and measurement diversity.

Numerical simulations demonstrate the effectiveness of the MMV approach.

The method improves imaging accuracy in polarization and sub-aperture diverse SAR data.

Abstract

We study a multiple measurement vector (MMV) approach to synthetic aperture radar (SAR) imaging of scenes with direction dependent reflectivity and with polarization diverse measurements. The data are gathered by a moving transmit- receive platform which probes the imaging scene with signals and records the backscattered waves. The unknown reflectivity is represented by a matrix with row support corresponding to the location of the scatterers in the scene, and columns corresponding to measurements gathered from different sub-apertures, or different polarization of the waves. The MMV methodology is used to estimate the reflectivity matrix by inverting in an appropriate sense the linear system of equations that models the SAR data. We obtain a resolution analysis of SAR imaging with MMV, which takes into account the sparsity of the imaging scene, the separation of the scatterers and the diversity of the measurements. The results of the analysis are illustrated with some numerical simulations.