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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Detection of Moving Ships Based on a Combination of Magnitude and Phase in Along-Track Interferometric SAR—Part II: Statistical Modeling and CFAR Detection
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Detection of Moving Ships Based on a Combination of Magnitude and Phase in Along-Track Interferometric SAR—Part II: Statistical Modeling and CFAR Detection

机译：航迹干涉SAR中基于幅度和相位组合的运动舰船检测—第二部分：统计建模和CFAR检测

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摘要

A new powerful constant false-alarm rate (CFAR) method is proposed for detecting moving ships in along-track interferometric synthetic aperture radar (ATI-SAR) images, based on the sea interferogram's magnitude and phase (SIMP) metric, presented in Part I of this paper. First, within the structure of the product model, a statistical model (hereafter simply termed ) for the square root of SIMP metrics of sea clutter was developed by assuming the radar cross-sectional (RCS) components of the return follow a commonly used gamma distribution. Moreover, the parameter estimators of the presented model were analytically derived by applying the well-known “method of log cumulants” (MoLC). Finally, the CFAR threshold of moving ship detection, using the proposed model was given analytically. The experimental results of measured ATI-SAR data by NASA/JPL's AirSAR show that the proposed method gives good performance.
机译：提出了一种新的强大的恒定误报率（CFAR）方法，该方法基于海洋干涉图的幅值和相位（SIMP）度量，用于检测沿航迹干涉式合成孔径雷达（ATI-SAR）图像中的动舰本文。首先，在产品模型的结构中，通过假设回程的雷达截面（RCS）分量遵循常用的伽马分布，开发了海杂波SIMP度量平方根的统计模型（以下简称为）。 。此外，通过应用众所周知的“对数累积量方法”（MoLC），可以分析得出本模型的参数估计量。最后，对提出的模型进行了动舰探测的CFAR阈值分析。 NASA / JPL的AirSAR实测ATI-SAR数据的实验结果表明，该方法具有良好的性能。

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