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

机译:DLSLA 3-D SAR跨轨重构的测量矩阵优化和失配问题补偿

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摘要

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.
机译:通过在交叉轨道方向上配置的短线性阵列,向下看的稀疏线性阵列三维合成孔径雷达(DLSLA 3-D SAR)可以获取成像场景的3-D图像。为了提高跨轨分辨率,近年来已经研究了稀疏恢复方法。在压缩感知(CS)框架中,重建性能取决于测量矩阵的属性。本文涉及一种优化测量矩阵并解决离网散射引起的测量矩阵失配的技术。在跨轨重建模型中,测量矩阵主要受天线相位中心(APC)配置的影响,因此,提出了两个基于互相关性的准则来优化APC的配置。另一方面,为了补偿测量矩阵的失配问题,通过联合估计散射体和离网误差,将基于稀疏贝叶斯推理的方法引入跨轨重构。实验证明了拟议的APC的配置方案和拟议的跨轨重建方法的性能。

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