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

delete2016-10-01
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AI
刘
刘保昌 (Baochang Liu) *
何
何宜军 (Yijun He)
DOI:10.1109/TGRS.2016.2582525delete
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摘要

摘要

En 中文
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 frequencymapping 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.
Keyword:
Inverse Omega-K (IOK) algorithm
ocean scenes
ocean-surface currents
ocean-surface waves
synthetic aperture radar (SAR)
synthetic aperture radar raw data simulation
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期刊

IEEE Transactions on Geoscience and Remote Sensing 封面图
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
论文数:
2.2W
被引数:
10.7W

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