SAR Image Reconstruction via Incremental Imaging With Compressive Sensing
Minseok Kang, Jaemin Baek
Abstract
For synthetic aperture radar (SAR) imaging, the irregular loss of received data and the nonuniformly under sampling yield the SAR azimuth ambiguity (SAA) resulting in the degradation in image quality. To address this issue, the incremental SAR imaging approach based on the estimation of sensing dictionary matrix in the pursuit of sparsity is presented in this article. Several beneficial contributions are included in the proposed method. First, the SAA reduction achievable in the proposed method is considerably improved more than that of the conventional compressive sensing (CS)-based approach in terms of the image quality and computational efficiency. Second, we established the signal parameterization scheme, which is divided into coarse and fine search steps to estimate the sensing matrix for SAR image restoration via signal model reconstruction. Third, an incremental imaging approach is devised to overcome the drawback of the conventional CS-based approach, which is not sufficiently good leading to limited SAA reduction performance under the nonsparse SAR image. These contributions are verified using numerical simulations and experimental results.