A New CFAR Matched Detector for an Autoregressive Model of Noise

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