Persymmetric Rao and Wald tests for adaptive detection of distributed targets in compound‐Gaussian noise

Abstract

The problem of detecting a distributed target in the presence of compound‐Gaussian noise with unknown covariance matrix is studied in this paper. Since no uniformly most powerful test exists for the problem at hand, two detectors based on the Rao and Wald tests are devised. Remarkably, the persymmetric structure of the covariance matrix is exploited in the design of the proposed detectors. Simulation results show that the proposed detectors outperform the traditional detectors, especially in training‐limited scenarios.