Qian Bao

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

With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of measurement matrix. This paper concerns the technique to optimize the measurement matrix and deal with the mismatch problem of measurement matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the measurement matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the measurement matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method.

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.创新点下视稀疏线性阵列三维合成孔径雷达(DLSLA 3-D SAR)常常由于跨航向的稀疏阵列安装条件受限等因素出现等效相位中心缺失和非均匀分布的情况,造成跨航向稀疏非均匀采样。对于具有稀疏性的3-D SAR成像场景,压缩感知(CS)方法能够在稀疏非均匀采样情况下获得良好的重构效果。然而,大多数CS算法都是基于离散假设,即假设散射点准确位于离散网格上;当真实散射点与离散网格不重合时,CS算法的重构效果将会受到网格偏离现象(off-grid effect)的影响。与离散的CS算法不同,原子范数最小化方法(ANM)直接在连续域上对稀疏信号进行恢复,不受网格偏离现象的影响。本文首先分析了DLSLA 3-D SAR跨航向稀疏重构时存在的网格偏离现象,然后提出了伪极坐标变换与原子范数最小化结合的成像算法。该算法首先通过距离压缩对波传播方向成像,然后对航迹向和跨航向进行伪极坐标变换,并通过傅里叶变换实现航迹向成像,然后在跨航向利用原子范数最小化方法进行成像。本文提出的方法能够在不同的成像场景中避免网格偏离现象、获得精确的成像结果。不同成像场景(点目标和分布式目标场景)的仿真实验成像结果验证了本文算法的有效性。

Holographic SAR Tomography Image Reconstruction by Combination of Adaptive Imaging and Sparse Bayesian Inference

In this letter, we propose an imaging algorithm for the holographic synthetic aperture radar tomography in the circumstance of sparse and nonuniform elevation circular passes. Considering the anisotropic behavior of scatterers and the off-grid effect of sparse signal recovery, the algorithm combines the 2-D adaptive imaging method for circular SAR and the sparse Bayesian inference-based method for elevation reconstruction. For each circular pass, the azimuth-range 2-D image can be formed by the adaptive imaging method, which depends on the preretrieved maximum azimuth response angle and the azimuth persistence width. To deal with the off-grid effect in elevation reconstruction, which is caused by the deviation between the true scatterers and the discretized imaging grids, the off-grid sparse Bayesian inference method jointly estimates the scatterers and elevation off-grid error by applying their hierarchical priors. Compared with the conventional compressive sensing method that does not concern the off-grid effect, the proposed algorithm can provide more accurate 3-D reconstruction for pointlike targets, which is verified by the real-data experiments.

Imaging method for downward-looking sparse linear array three-dimensional synthetic aperture radar based on reweighted atomic norm

Abstract. We propose an imaging algorithm for downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) in the circumstance of cross-track sparse and nonuniform array configuration. Considering the off-grid effect and the resolution improvement, the algorithm combines pseudo-polar formatting algorithm, reweighed atomic norm minimization (RANM), and a parametric relaxation-based cyclic approach (RELAX) to improve the imaging performance with a reduced number of array antennas. RANM is employed in the cross-track imaging after pseudo-polar formatting the DLSLA 3-D SAR echo signal, then the reconstructed results are refined by RELAX. By taking advantage of the reweighted scheme, RANM can improve the resolution of the atomic norm minimization, and outperforms discretized compressive sensing schemes that suffer from off-grid effect. The simulated and real data experiments of DLSLA 3-D SAR verify the performance of the proposed algorithm.

Enhanced Three-Dimensional Imaging for Multi-Circular Synthetic Aperture Radar

In multi-circular synthetic aperture radar (MCSAR) mode, resolution and sidelobes are two important parameters to consider when representing imaging quality, as in other SAR imaging modes. In this paper, three-dimensional (3-D) resolution and cone-shaped sidelobes of MCSAR are analyzed for a point target in the scene center under the Nyquist sampling criterion. The results of the analysis show that resolution can be improved, and cone-shaped sidelobes can be suppressed by increasing the system bandwidth and the length of synthetic aperture in the elevation direction. But this will make the system of acquiring data more difficult. It turns out that some digital signal processing techniques can enhance 3-D imaging quality of MCSAR. In this paper, a simple method based on spectrum extrapolation and interferometric phase masking is proposed to improve 3-D resolution and suppress cone-shaped sidelobes of MCSAR. Experimental results regarding a tank model in a microwave anechoic chamber demonstrate that this method is effective.

Measurement matrix optimization schemes for DLSLA 3-D SAR cross-track reconstruction based on mutual coherence criterions

Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of the true scene, and compressive sensing (CS) provide the solution for cross-track super-resolution reconstruction. This paper concerns the issue of optimizing the measurement matrix, which affects the reconstruction performance. Two mutual coherence based criteria are proposed to optimize the measurement matrix, and the experiments verify the proposed algorithms.

Gridless sparse recovery methods for DLSLA 3-D SAR crosstrack reconstruction

Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction into the problem of sparse signal recovery, we introduce two kinds of gridless sparse recovery (GL-SR) methods to DLSLA 3-D SAR cross-track imaging, i.e., atomic norm minimization (ANM) and gridless SPICE (GLS). Compared with the conventional grid-based sparse recovery (GB-SR) methods, which assume that the scatterers are exactly on the discretized grids, the GL-SR methods can avoid the off-grid effect. Experiments compare the performance of GB-SR and GL-SR methods for DLSLA 3-D SAR cross-track reconstruction.

Full-aspect 3D target reconstruction of interferometric circular SAR

Circular SAR has several attractive features, such as full-aspect observation, high resolution, and 3D target reconstruction capability, thus it has important potential in fine feature description of typical targets. However, the 3D reconstruction capability relies on the scattering persistence of the target. For target with a highly directive scattering property, the resolution in the direction perpendicular to the instantaneous slant plane is very low compared to the range and azimuth resolutions, and the 3D structure of target can hardly be obtained. In this paper, an Interferometric Circular SAR (InCSAR) method is proposed to reconstruct the full-aspect 3D structure of typical targets. InCSAR uses two sensors with a small incident angle difference to collect data in a circular trajectory. The method proposed in this paper calculates the interferometric phase difference (IPD) of the image pair at equally spaced height slices, and mask the original image with an IPD threshold. The main principle is that when a scatterer is imaged at a wrong height, the image pair has an offset, which results in a nonzero IPD, and only when the scatterer is correctly imaged at its true height, the IPD is near zero. The IPD threshold is used to retain scatterers that is correctly imaged at the right height, and meanwhile eliminate scatterers that is imaged at a wrong height, thus the 3D target structure can be retrieved. The proposed method is validated by real data processing, both the data collected in the microwave chamber and the GOTCHA airborne data.