In this study, the authors address the problem of range-distributed target detection in sequential high resolution range profiles (HRRPs). They propose a modified scaled largest eigenvalue detector for static target in homogenous ground clutter. A set of secondary data, which are free of target signal and have the same distribution as the clutter in the primary data, are assumed to be available. First, the sample covariance matrix (SCM) is estimated from the acquired multiple HRRPs in a short coherent processing interval. Then, the eigenvalue decomposition of the SCM is performed, and the eigenvalues are sorted in descending order. Finally, the largest eigenvalue scaled by the noise power estimated from the secondary data is selected as the detection statistic. Compared with existing methods of largest eigenvalue-based detection, the proposed method achieves better detection performance for coloured clutter by considering secondary data. Numerical and experimental results demonstrate the effectiveness of the proposed method.

中文翻译：

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高分辨率距离剖面中基于特征值的地面目标检测

在这项研究中，作者解决了顺序高分辨率距离剖面（HRRP）中的距离分布目标检测问题。他们为均匀地面杂波中的静态目标提出了一种改进的缩放最大特征值检测器。假定一组可用的辅助数据不存在目标信号，并且与原始数据中的杂波具有相同的分布。首先，在较短的相干处理间隔中，从获取的多个HRRP中估计样本协方差矩阵（SCM）。然后，执行SCM的特征值分解，并按降序对特征值进行排序。最后，将根据从辅助数据估计的噪声功率缩放的最大特征值选择为检测统计量。与现有最大的基于特征值的检测方法相比，通过考虑二次数据，该方法对彩色杂波具有更好的检测性能。数值和实验结果证明了该方法的有效性。