A Persymmetric Algorithm for Distributed Target Detection in Subspace Interference Plus Partially Homogeneous Clutter

Abstract

To effectively detect distributed targets amid subspace interference and partially homogeneous clutter, a persymmetric detector is designed based on the generalized likelihood ratio test. Assume that the radar target and interference signals lie in two linearly independent subspaces, with deterministic but unknown coordinates, and the clutter covariance matrix structure is identical in both the test and training data, but the clutter power levels differ. Moreover, the existence of persymmetric structures in the clutter covariance matrix is leveraged to reduce the amount of training data required. Theoretical analyses demonstrate that the proposed detector maintains a constant false alarm rate across varying unknown clutter covariance matrix and the clutter power levels. Moreover, the numerical findings indicate that the proposed detector demonstrates an enhanced ability to suppress interference and excels its counterparts in detection performance, for both the cases of matched and mismatched subspace signals, particularly in scenarios with limited training data sizes.