Bao Qian, Jiang Chenglong, Lin Yun, Tan Weixian, Wang Zhirui, Hong Wen
Science and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences (IECAS), Beijing 100190, China.
University of Chinese Academy of Sciences (UCAS), Beijing 100190, China.
Sensors (Basel). 2016 Aug 22;16(8):1333. doi: 10.3390/s16081333.
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.
采用沿航迹方向配置的短线性阵列，下视稀疏线性阵列三维合成孔径雷达（DLSLA 3-D SAR）能够获取成像场景的三维图像。为提高沿航迹分辨率，近年来对稀疏恢复方法进行了研究。在压缩感知（CS）框架下，重建性能取决于测量矩阵的性质。本文关注优化测量矩阵以及处理由离网格散射体引起的测量矩阵失配问题的技术。在沿航迹重建模型中，测量矩阵主要受天线相位中心（APC）配置的影响，因此，提出了基于两个互相关的准则来优化APC的配置。另一方面，为补偿测量矩阵的失配问题，通过联合估计散射体和离网格误差，将基于稀疏贝叶斯推理的方法引入沿航迹重建。实验证明了所提出的APC配置方案和沿航迹重建方法的性能。
