Rao and Wald Tests for Distributed Target Detection in Compound-Gaussian Clutter with Gamma Texture

Abstract

We focus on the problem of detecting a distributed target in compound-Gaussian clutter, where the texture is a random variable with Gamma distribution. The Rao and Wald tests are devised by using the two-step method: in the first step, the Rao and Wald tests are designed by assuming that the texture and covariance matrix structure are known; in the second step, the maximum a posterior probability estimate of clutter texture and the fixed point estimate of covariance matrix structure are used to replace the known texture and covariance matrix in the tests derived in the first step, respectively. The effectiveness of the proposed detectors is verified by using simulated and real data.