Persymmetric adaptive detectors design in the presence of deterministic subspace interference

In the adaptive detection process of target signal under interference and Gaussian clutter, the issue of insufficient training samples often degrades the performance of detectors. To this end, we exploit the persymmetry of clutter covariance matrix to enhance the detection performance. The interference and target signal are described as subspace models, namely, it is assumed to lie in different deterministic subspaces, but with unknown coordinates. Above that, two persymmetric adaptive detectors are designed resorting to the Gradient and Durbin criteria, respectively. Numerical Monte Carlo experimental examples indicate that these persymmetric detectors exhibit superior performance to existing counterparts in some scenarios and possess the constant false alarm rate (CFAR) property.