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Polarimetric detection and range estimation of a point-like target

机译：点状目标的极化检测和距离估计

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摘要

In this paper, we deal with the problem of polarimetric diversity detection for point-like targets in the presence of Gaussian clutter with unknown covariance matrix. To this end, we jointly exploit the polarization diversity and the spillover of target energy to consecutive range samples to improve the performances of detection and range localization. For estimation purposes, we assume that a set of secondary data (free of signal components) is available with the same covariance matrix as the clutter in the cells under test. Because the uniformly most powerful test does not exist for this problem, we derive two adaptive detectors: the generalized likelihood ratio test and the Wald test. Interestingly, these new receivers ensure the constant false alarm rate property with respect to covariance matrix of the clutter. The performance assessments conducted on both simulated data and real recorded dataset reveal that the proposed detectors outperform, in both detection and localization, the traditional state-of-the-art counterparts that ignore either the polarimetry or the spillover.
机译：在本文中，我们解决了在高斯杂波存在协方差矩阵未知的情况下，针对点状目标进行极化分集检测的问题。为此，我们共同利用极化分集和目标能量向连续范围样本的溢出来改善检测和范围定位的性能。为了进行估计，我们假设一组辅助数据（不含信号分量）与被测单元中的杂波具有相同的协方差矩阵。由于没有针对此问题的功能最强大的统一测试，因此我们得出了两个自适应检测器：广义似然比测试和Wald测试。有趣的是，这些新的接收器可确保相对于杂波的协方差矩阵具有恒定的误报率属性。在模拟数据和实际记录的数据集上进行的性能评估表明，在检测和定位方面，拟议的检测器均优于传统的最新技术，而后者却忽略了极化技术或溢出技术。

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