Persymmetric adaptive subspace detection in compound Gaussian sea clutter with generalized inverse Gaussian texture

This paper considers the detection problem of subspace targets in compound Gaussian sea clutter with generalized inverse Gaussian (GIG) texture. Based on the Wald test, the gradient test, and the Durbin test, we propose three novel subspace detectors. And the criterion above are first applied to the detection problem in compound Gaussian (CG) sea clutter with GIG texture. In the first step, assuming that the speckle covariance matrix (CM) and the texture component are unknown to obtain the test statistics of the proposed detectors. In the second step, we exploit the persymmetric property of speckle CM and the maximum a posteriori (MAP) criterion to obtain the estimation of speckle CM and the GIG texture parameters. Moreover, the constant false alarm rate (CFAR) properties of the three proposed detectors have been proven with respect to the speckle CM and the scale parameter of the texture component. We verify the detection performance of the three proposed detectors by the numerical experiments both in simulated data and real sea clutter data, and the simulated results show the proposed detectors are robust in the case of limited training and mismatched signals.