Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter

In this paper, we consider the adaptive detection problem of range-spread targets embedded in subspace interference plus structured Gaussian clutter. The target signal and interference are assumed to lie in two linearly independent subspaces with unknown coordinates. The clutter component is modeled as a complex Gaussian vector with an unknown persymmetric covariance matrix. We leverage the persymmetric structure to design a two-step detector according to the Rao test criterion. The theoretical results show that the proposed detector possesses the constant false alarm rate property with respect to the clutter covariance matrix. Furthermore, the numerical results show that the proposed detector exhibits better detection performance than the existing unstructured subspace detectors, particularly under a limited training data size. In addition, the proposed detector outperforms the existing persymmetric subspace detectors.