Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency

TL;DR

This paper introduces an efficient gridless TomoSAR imaging method using an accelerated atomic norm minimization algorithm that reduces computational complexity while maintaining high-resolution 3D target localization.

Contribution

It proposes the IVDST-ANM algorithm, significantly decreasing the computational load of atomic norm minimization for TomoSAR elevation focusing with single snapshot capability.

Findings

The proposed method effectively reconstructs 3D targets from simulated data.

It achieves high-resolution imaging with reduced computational resources.

Experimental results on urban data demonstrate practical applicability.

Abstract

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented.