Subspace-based distributed target detection in compound-Gaussian clutter

Abstract

This paper investigates the problem of radar detection of distributed targets embedded in unknown compound-Gaussian clutter. To solve the problem, we first derive two detectors for fixed covariance matrix according to Rao and Wald tests with the use of order statistics theory. Then, the unknown covariance matrix is estimated by the approximate maximum likelihood (AML) estimation. Numerical examples through simulated and real data show that the Wald test-based detector has a higher probability of detection (PD) than existing detectors.