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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Target prescreening based on a quadratic gamma discriminator
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Target prescreening based on a quadratic gamma discriminator

机译：基于二次伽玛鉴别器的目标预筛选

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摘要

This work presents the development, analysis and validation of a new target discrimination module for synthetic aperture radar (SAR) imagery based on an extension of gamma functions to 2-D. Using the two parameter constant false-alarm rate (CFAR) stencil as a prototype, a new stencil based on 2-D gamma functions is used to estimate the intensity of the pixel under test and its surroundings. A quadratic discriminant function is created from these estimates, which is optimally adapted with least squares in a training set of representative clutter and target chips. This discriminator is called the quadratic gamma discriminator (QGD). The combination of the CFAR and the QGD was tested in realistic SAR environments and the results show a large improvement of the false alarm rate with respect to the two-parameter CFAR, both with high resolution (1 ft) fully polarimetric SAR and with one polarization, 1 m SAR data.
机译：这项工作提出了一种新的目标识别模块的开发，分析和验证，该模块基于将伽马函数扩展到二维的合成孔径雷达（SAR）图像。使用两个参数的恒定误报率（CFAR）模板作为原型，基于2-D伽马函数的新模板用于估计被测像素及其周围环境的强度。从这些估计中创建一个二次判别函数，该函数在具有代表性的杂波和目标芯片的训练集中以最小二乘方最佳地进行了适配。该鉴别器称为二次伽马鉴别器（QGD）。在现实的SAR环境中测试了CFAR和QGD的组合，结果表明，相对于两参数CFAR而言，在高分辨率（1 ft）全极化SAR和一种极化情况下，虚警率都有很大提高，1 m SAR数据。

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