Autofocus

Radar imaging

Algorithm

Image (mathematics)

Optics

Physics

Compressed sensing

Image formation

Mathematical analysis

Inverse problem

Iterative reconstruction

Tikhonov regularization

Artificial intelligence

Azimuth

Focus (optics)

Geometry

Regularization (linguistics)

Computer vision

Mathematics

Computer science

Image quality

Radar

Telecommunications

Synthetic aperture radar

IEEE Geoscience and Remote Sensing Letters

In synthetic aperture radar (SAR) imaging, perturbations in the motion of the moving platform induce an undesired phase error due to imprecise knowledge of the motion, which results in the significant degradations in the quality of SAR images. In this paper, we present an efficient compressive sensing (CS)-based SAR imaging integrated with autofocus technique. The novel approach is based on an estimation-theoretic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm-based approach coupled with Tikhonov-type regularization which combines an observation model of the SAR image formation process with the CS reconstruction problem of the SAR image regarding the sparsity. The optimization problem derived by considering spatially variant phase errors along azimuth domain and the dataset sampled at low rates can be effectively addressed by an efficient iterative method, wherein each iteration both SAR image formation and phase error correction are simultaneously carried out. The simulations and experimental results are presented to validate the effectiveness of the proposed method in terms of reliable image recovery and efficient computational complexity.

Efficient SAR Imaging Integrated With Autofocus via Compressive Sensing