Subspace gradient tests for range‐spread target detection in structured interference plus Gaussian clutter

Abstract

AbstractThis study considers the problem of detecting range‐spread targets embedded in subspace interference plus Gaussian clutter with an unknown covariance matrix. The target and interference signals are modeled in terms of deterministic signals belonging to two known subspaces, respectively. Based on the Gradient test criterion, two adaptive detectors are devised for rejecting subspace interference in homogeneous and partially homogeneous environments, respectively. Both of the proposed detectors theoretically exhibit a desirable property of a constant false alarm rate with respect to the clutter covariance matrix as well as the power level. Furthermore, the numerical results show that, compared with their existing counterparts, the proposed detectors exhibit better detection performance and satisfactory suppression performance for the interference.