Persymmetric Adaptive Radar Target Detection in CG-LN Sea Clutter Using Complex Parameter Suboptimum Tests

In this article, we consider the detection problem of marine radar targets embedded in correlated non-Gaussian sea clutter, which is modeled by a compound Gaussian model with lognormal texture (CG-LN) and unknown covariance matrices. To reduce the dependence of detectors on training data, the original radar data are transformed via exploiting the persymmetric structure of clutter covariance matrix. Then, four complex parameter suboptimum tests, which are the Rao, Wald, Gradient, and Durbin tests, are used to design the adaptive persymmetric coherent detectors for radar targets. It is shown that the Gradient test and the Durbin test coincide with the Rao test for the problem of radar target detection in CG-LN sea clutter. We prove that the proposed persymmetric Rao detector with lognormal texture (PRAO-LND) and persymmetric WALD detector with lognormal texture (PWALD-LND) can ensure the constant false alarm rate (CFAR) with respect to the clutter power mean and the clutter speckle covariance matrix. Experimental results on simulated and measured radar data show that the proposed PRAO-LND performs better than its competitors and is robust to the mismatched signals. Moreover, the proposed PWALD-LND has asymptotic performance with the proposed PRAO-LND, when the non-Gaussianity of sea clutter is weakened, and has good selectivity with signal mismatch.