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SAR Raw Data Simulation for Ocean Scenes Using Inverse Omega-K Algorithm

机译:使用逆Omega-K算法模拟海洋场景的SAR原始数据

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This paper deals with synthetic aperture radar (SAR) raw data simulation for ocean scenes featuring surface waves and currents, an issue which has proven to be of great necessity in preparing for future oceanic SAR missions. In this paper, the inverse Omega-K (IOK) algorithm, which is originally designed for SAR raw data simulation of stationary land scenes, is extended to ocean scenes. To realize such a generalization, endeavors are made in two aspects. First, specially aimed at ocean dynamics of ocean waves and currents, the 2-D spectrum of the SAR signal is derived. Second, to account for the spatial variation of ocean-motion parameters, we adopt a strategy called batch processing, whose basic feature is that a single implementation of the IOK algorithm will simultaneously simulate a collection of ocean-surface backscattering elements that have the same radial velocity. For the proposed simulator, the velocity bunching effect is embodied via making the long-wave radial orbital velocities physically enter the range equation of the SAR raw signal, instead of superimposing this effect onto the reflectivity map. The spread of the facet velocities within one resolution cell enters the raw data through a random perturbation of the local long-wave orbital velocity. The proposed simulator is not only rather accurate due to the fact that, in deriving the range frequency mapping function, no Taylor expansion is made on the range equation, but also much more efficient than its time-domain counterpart. Effectiveness of the proposed simulator is validated by using simulation results.
机译:本文针对具有表面波和水流的海洋场景进行合成孔径雷达(SAR)原始数据仿真,事实证明,在准备未来的海洋SAR任务时,这一问题非常必要。本文将最初用于固定陆地场景的SAR原始数据仿真的Omega-K逆算法(IOK)扩展到海洋场景。为了实现这样的概括,在两个方面做出了努力。首先,专门针对海浪和洋流的海洋动力学,得出SAR信号的二维频谱。其次,为了解决海洋运动参数的空间变化,我们采用了一种称为批处理的策略,其基本特征是IOK算法的单一实现将同时模拟具有相同径向方向的海面后向散射元素的集合。速度。对于拟议的模拟器,通过使长波径向轨道速度物理上进入SAR原始信号的距离方程,而不是将这种影响叠加到反射率图上,来体现速度聚集效应。在一个分辨率单元中,小面速度的分布是通过局部长波轨道速度的随机扰动输入原始数据的。所提出的仿真器不仅非常精确,这是因为在推导范围频率映射函数时,没有对范围方程式进行泰勒展开,而且比其时域对应方法效率更高。通过仿真结果验证了所提出仿真器的有效性。

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