Adaptive Persymmetric Subspace Detectors in the Partially Homogeneous Environment

摘要: This paper addresses the adaptive detection of subspace signals in noise whose covariance matrix is unknown. The partially homogeneous scenario, where primary data have same with training up to an unknown scaling factor considered. We exploit persymmetric structure enhance matched performance case limited number data. Three detectors are proposed by applying generalized likelihood ratio (GLR), Rao and Wald design criteria, respectively. It proved that three can ensure constant false alarm rate (CFAR) property. Experimental results show new significantly outperform conventional detector terms performance. Compared rank-one signal detectors, more robust mismatched case.