Focusing highly squinted data with motion errors based on modified non-linear chirp scaling
Focusing highly squinted data with motion errors based on modified non-linear chirp scaling
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DOI：
10.1049/iet-rsn.2012.0134
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发表时间：
2013-06
影响因子：
1.7
通讯作者：
Liu Gao-gao;Peng Li;Shiyang Tang;Linrang Zhang
中科院分区：
文献类型：
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作者：
Liu Gao-gao;Peng Li;Shiyang Tang;Linrang Zhang
在机载合成孔径雷达配置中，运动补偿(MOCO)对补偿大气湍流和/或飞机机动引起的运动误差具有重要作用。在侧面或小斜视的情况下，典型的两步MOCO可以获得聚焦良好的图像，并对二次运动误差进行补偿。然而，随着斜视角度的增大，不仅要考虑高阶运动误差，还要考虑方位变化误差。在这项研究中，提出了一种改进的非线性Chirp Scaling(MNLCS)算法来处理大斜视情况下的这一问题。关键是用级数反演法覆盖高阶运动误差，用MNLCS对数据进行预处理以处理方位变化分量，并用级数展开得到精确的信号频谱形式。通过使用线性范围单元偏移校正，减小了大斜视情况下的偏斜频谱。仿真结果表明，MNLCS算法能够处理比原算法更复杂的数据。
The motion compensation (MOCO) plays a significant role to accommodate the motion errors caused by atmosphere turbulence and/or aircraft maneuvers in airborne synthetic aperture radar configurations. In broadside or small squint cases, the well-focused image can be obtained by the typical two-step MOCO to have the quadratic motion errors compensated. However, with the increasing of squint angle, not only the high-order motion errors, but also the azimuth-variant errors must be taken into account. In this study, a modified non-linear chirp scaling (MNLCS) algorithm is proposed to handle this problem in highlysquinted case. The key is to use the method of series reversion to cover the high-order motion errors, the MNLCS to precondition the data to process the azimuth-variant components and a series expansion to obtain an accurate form of the signal spectrum. The skewed spectrum in highly-squinted case is reduced through the use of a linear range cell migration correction. The simulated results have shown the MNLCS algorithm can handle data with more complicated geometries than the previous algorithm.