Sparse Autofocus Recovery for Under-Sampled Linear Array Sar 3-D Imaging

Abstract

Abstract-Linear array synthetic aperture radar (LASAR) is a promising radar 3-D imaging technique. In this paper, we address the problem of sparse recovery of LASAR image from under-sampled and phase errors interrupted echo data. It is shown that the unknown LASAR image and the nuisance phase errors can be constructed as a bilinear measurement model, and then the under-sampled LASAR imaging with phase errors can be mathematically transferred into sparse signal recovery by solving an ill-conditioned constant modulus linear program (ICCMLP) problem. Exploiting the prior sparse spatial feature of the observed targets, a new super-resolution sparse autofocus recovery algorithm is proposed for under-sampled LASAR 3-D imaging. The algorithm is an iterative minimize estimation procedure, wherein it converts the ICCMLP into two independent convex optimal problems, and joints 1 -norm reweights least square regularization and semi-definite relax technique to find the optimal solutions. Simulated and experimental results confirm that the proposed method outperforms the classical autofocus techniques in under-sampled LASAR imaging.

Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction

“…With linear array antennas distributed along the wing of a rectilinearly moving platform and a downward-looking imaging geometry, DLSLA 3-D SAR can synthesize a 2-D plane array and obtain the 3-D resolution of the imaging scene [1,2,3,4,5,6]. …”

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

“…Going one step further, one can perform autofocusing and imaging simultaneously in a sparsity-driven framework, which has been shown to produce promising results [5], [34]- [37]. As an example of such an approach, the sparsitydriven autofocus (SDA) method [34] for an isotropic scattering scenario is based on the following observation model in which phase errors are considered as model errors:…”

“…A total variation penalty on the field is incorporated into the optimization problem as well. In [37], the idea of joint sparsity-driven imaging and autofocusing is used for 3D imaging based on undersampled linear array SAR data.…”

Along-Track Motion Compensation for Strip-Map Sar Based on Resampling

“…And the forward velocity variation and the displacement in line of sight (LOS) affect respectively the lower and higher order terms of the Doppler frequency, so that they can be extracted sequentially. According to this, several algorithms [11] are proposed, such as the reflectivity displacement method (RDM) [12], the phase gradient algorithm (PGA) [13,14,16] and the phase retrieval [17] methods.…”

“…However, one of the problems is that the data rate of INU or GPS is usually much lower than the azimuth sampling frequency, while the interpolation may bring about extra errors and reduce the precision. The other commonly used way to obtain the motion error is to estimate the trajectory deviation directly from the raw data [7][8][9][10][11][12][13][14][15][16][17]. It is based on the concept that the uniformly azimuth-sampled data without motion errors has its intrinsic coherence, while the motion error is random.…”