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Covariance matrix estimation for adaptive CFAR detection incompound-Gaussian clutter

机译：复合高斯杂波中自适应CFAR检测的协方差矩阵估计

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摘要

We address the estimation of the structure of the covariance matrix and its application to adaptive radar detection of coherent pulse trains in clutter-dominated disturbance modeled as a compound-Gaussian process. For estimation purposes we resort to range cells in spatial proximity with that under test and assume that these cells, free of signal components, can be clustered into groups of data with one and the same value of the texture. We prove that, plugging the proposed estimator of the structure of the covariance matrix into a previously derived detector, based upon the generalized likelihood ratio test (GLRT), leads to an adaptive detector which ensures the constant false alarm rate (CFAR) property with respect to the clutter covariance matrix as well as the statistics of the texture. Finally, we show that this adaptive receiver has an acceptable loss with respect to its nonadaptive counterpart in cases of relevant interest for radar applications
机译：我们讨论了协方差矩阵的结构估计及其在以复合高斯过程建模的杂波为主干扰中相干脉冲序列的自适应雷达检测中的应用。为了进行估计，我们求助于与被测对象在空间上接近的距离像元，并假设这些不含信号分量的像元可以聚类为具有相同纹理值的数据组。我们证明，基于广义似然比测试（GLRT），将协方差矩阵的结构的估计器插入到先前导出的检测器中，会导致自适应检测器，该检测器可确保相对于恒定的误报率（CFAR）属性杂乱的协方差矩阵以及纹理的统计信息。最后，我们表明，在与雷达应用相关的情况下，该自适应接收机相对于其非自适应接收机具有可接受的损耗

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