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Effects of Mismatched Training on Adaptive Detection

机译：不匹配训练对自适应检测的影响

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摘要

Interference cancelation in the adaptive radar detection context typically relies on training samples to estimate the covariance matrix of interference and noise in the test vector. Adaptive detection algorithms are generally developed under the assumption that the interference-plus-noise covariance matrix of the test vector (say C) is the same as the interference-plus-noise covariance matrix of the training vectors (say Σ). When the two covariance matrices are not perfectly matched the constant false alarm rate (CFAR) feature of adaptive detectors is no longer valid. For mismatched conditions, standard scalar CFAR techniques can be applied on adaptive detector outputs to regain the CFAR feature. In this paper we consider the Adaptive Matched Filter (AMF) statistic based CFAR detector and shown that the effects of covariance matrix mismatch can be condensed into a single scalar quantity referred to as the loss factor ρ. The loss factor is a random variable if the estimate of Σ is a random matrix. Sample results are provided for the deterministic case.
机译：自适应雷达检测上下文中的干扰抵消通常依赖于训练样本来估计测试矢量中干扰和噪声的协方差矩阵。通常在假设测试向量（例如c）的干扰 - 加噪声协方差矩阵与训练向量的干扰 - 加噪声协方差矩阵相同（例如Σ）的情况下开发自适应检测算法。当两个协方差矩阵没有完全匹配时，自适应检测器的常量误报率（CFAR）特征不再有效。对于不匹配的条件，可以在自适应检测器输出上应用标准标量CFAR技术以重新获得CFAR功能。在本文中，我们考虑自适应匹配滤波器（AMF）基于统计的CFAR检测器，并示出了协方差矩阵失配的效果可以被冷凝成称为损耗因子ρ的单个标量量。如果σ是随机矩阵的估计，则损耗因子是随机变量。为确定性案例提供了样本结果。

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