Persymmetric Map Detection in Compound Gaussian Sea Clutter with Gamma Texture

This paper addresses the adaptive persymmetric detection of range-spread targets problem in compound Gaussian sea clut-ter with Gamma texture. A new compound Gaussian detector based on two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is developed. Specifically, the first step is to acquire the test statistic of GLRT on the as-sumption that the texture components and the clutter covari-ance matrix (CCM) are known. The second step is to employ the MAP approach to acquire the estimates of texture components and persymmetric method to obtain the estimate of CCM. Remarkably, the novel detector ensures the constant false alarm rate (CFAR) property with respect to the covari-ance matrix structure. The effectiveness of the proposed de-tector is verified by using simulated and real sea clutter data.