Zhou Xiaoli, Wang Hongqiang, Cheng Yongqiang, Qin Yuliang
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2015 Oct 30;15(11):27611-24. doi: 10.3390/s151127611.
Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. The sparsity-driven approaches are commonly used in RCI, while the prior knowledge of imaging models needs to be known accurately. However, as one of the major model errors, the gain-phase error exists generally, and may cause inaccuracies of the model and defocus the image. In the present report, the sparse auto-calibration method is proposed to compensate the gain-phase error in RCI. The method can determine the gain-phase error as part of the imaging process. It uses an iterative algorithm, which cycles through steps of target reconstruction and gain-phase error estimation, where orthogonal matching pursuit (OMP) and Newton's method are used, respectively. Simulation results show that the proposed method can improve the imaging quality significantly and estimate the gain-phase error accurately.
雷达重合成像（RCI）是一种高分辨率凝视成像技术，不受目标与雷达之间相对运动的限制。稀疏驱动方法常用于RCI中，而成像模型的先验知识需要准确知晓。然而，作为主要的模型误差之一，增益相位误差普遍存在，可能导致模型不准确并使图像散焦。在本报告中，提出了稀疏自校准方法来补偿RCI中的增益相位误差。该方法可以在成像过程中确定增益相位误差。它使用一种迭代算法，该算法循环执行目标重建和增益相位误差估计步骤，其中分别使用了正交匹配追踪（OMP）和牛顿法。仿真结果表明，所提方法能显著提高成像质量并准确估计增益相位误差。
