论文标题
协方差不匹配的培训样本对持续错误警报率检测器的影响
Impact of covariance mismatched training samples on constant false alarm rate detectors
论文作者
论文摘要
本文的框架是高斯噪声中具有未知协方差矩阵的自适应检测的框架，当训练样本没有与正在测试的向量相同的协方差矩阵时。我们考虑一类恒定的错误警报率检测器，该检测器取决于两个统计$（β，\ ttilde）$，其分布在没有不匹配的情况下是无参数的，我们分析了协方差不匹配的培训样本的影响。更确切地说，我们为任意不匹配提供了这两个变量的统计表示。我们表明，协方差不匹配引起了错误警报概率的显着变化，我们研究了一种减轻这种效果的方法。
The framework of this paper is that of adaptive detection in Gaussian noise with unknown covariance matrix when the training samples do not share the same covariance matrix as the vector under test. We consider a class of constant false alarm rate detectors which depend on two statistics $(β,\ttilde)$ whose distribution is parameter-free in the case of no mismatch and we analyze the impact of covariance mismatched training samples. More precisely, we provide a statistical representation of these two variables for an arbitrary mismatch. We show that covariance mismatch induces significant variations of the probability of false alarm and we investigate a way to mitigate this effect.
