Radar imaging

Gene

Algorithm

Residual

Materials science

Robustness (evolution)

Range (aeronautics)

Telecommunications

Inverse synthetic aperture radar

Biochemistry

Computer science

Geology

Physics

Optics

Synthetic aperture radar

Focus (optics)

Artificial intelligence

Remote sensing

Composite material

Azimuth

Chemistry

Radar

Autofocus

Computer vision

IEEE Transactions on Geoscience and Remote Sensing

With continuous improvement of the synthetic aperture radar (SAR) resolution, the 2-D defocus effect in SAR image resulting from uncompensated motion error has made autofocus become a new challenging problem. Conventional 2-D autofocus approaches assume that the 2-D phase error is absolutely unknown and estimate them directly. Due to high dimensionality of the unknown parameters, these approaches often suffer from high computational burden and low estimate accuracy. In this paper, we analyze the effect of range migration algorithm (RMA) processing on the 2-D echo phase, and reveal the analytical structure of the residual 2-D phase error in RMA imagery. Then, by exploiting the derived prior knowledge on the phase error structure, an accurate and efficient 2-D autofocus approach is proposed. In the new method, only 1-D error, e.g., azimuth phase error, or residual range cell migration, is required to be estimated directly, while the 2-D phase error is computed directly from the estimated 1-D error by exploiting the analytical structure of the 2-D phase error. The experimental results clearly demonstrate the effectiveness and robustness of the proposed method.

Knowledge-Aided 2-D Autofocus for Spotlight SAR Range Migration Algorithm Imagery