Compressive Sensing-Based Omega-K Algorithm for SAR Focusing
Minseok Kang, Jaemin Baek
Abstract
In this letter, a novel approach to compressive sensing (CS)-based synthetic aperture radar (SAR) focusing is presented based on an approximate version of an omega-K algorithm (A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> A) without the Stolt interpolation. The optimal A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> A operator (A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega $ </tex-math></inline-formula> O) for SAR focusing, based on the CS framework, is formulated as a linear system that converts the received raw data into the SAR image. The entire process of the proposed method can be efficiently carried out using a rapid matrix-vector multiplication operations. The image reconstruction process is implemented two-dimensionally, covering simultaneously both the range and azimuth domains from a small number of the raw data. Several experimental results using both simulated and measured data show that proposed method is effective in creating SAR images from subsampled data.