Elevation Spatial Variation Error Compensation in Complex Scene and Elevation Inversion by Autofocus Method in GEO SAR

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion.

Improvement of Stripmap SAR Autofocus Based on Minimum-Entropy Criterion

Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

Backprojection Autofocus of Large Ships with Arbitrary Motion for Synthetic Aperture Radar

Radar images of the open sea taken by airborne synthetic aperture radar (SAR) show typically several smeared ships. Due to their non-linear motions on a rough sea, these ships are smeared beyond recognition, such that their images are useless for classification or identification tasks. The ship imaging algorithm presented in this thesis consists of a fast image reconstruction using the fast factorized backprojection algorithm and an extended autofocus algorithm of large moving ships. This thesis analysis the factorization parameters of the fast factorized backprojection algorithm and describes how to choose them nearoptimally in order to reconstruct SAR images with minimal computational costs and without any loss of quality. Furthermore, this thesis shows how to estimate and compensate for the translation, the rotation and the deformation of a large arbitrarily moving ship in order to reconstruct a sharp image of the ship. The proposed autofocus technique generates images in which the ...