A New CFAR Matched Detector for an Autoregressive Model of Noise

V. Golikov, O. Lebedeva
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Abstract

The constant false alarm rate (CFAR) matched detector (CFAR MD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. The CFAR adaptive subspace detector (CFAR MD) was proposed for detecting a target signal in noise whose covariance structure and level are both unknown. In this paper, we use the theory of GLRTs to adapt the no-adaptive CFAR MDs to unknown noise covariance matrices with autoregressive (AR) structure. In this situation, we proposed a new CFAR NCFMD whose structure does not depend on noise covariance matrix and level and its performance penalty is small
一种新的自回归噪声模型CFAR匹配检测器
恒虚警率(CFAR)匹配检测器(CFAR MD)是在协方差结构已知但电平未知的噪声中检测目标信号的一致最有效的不变检验和广义似然比检验(GLRT)。针对噪声中协方差结构和电平未知的目标信号,提出了一种CFAR自适应子空间检测器(CFAR MD)。在本文中,我们利用glrt理论对具有自回归(AR)结构的未知噪声协方差矩阵进行非自适应CFAR模型的自适应。在这种情况下,我们提出了一种新的CFAR NCFMD,其结构不依赖于噪声协方差矩阵和电平,性能损失小
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