Adaptive detection in nonzero-mean compound gaussian sea clutter with inverse gamma texture

This paper deals with the target detection problem in nonzero-mean compound Gaussian sea clutter with inverse Gamma texture. Considering the improvement of radar resolution and the time-varying characteristics of real sea clutter, the compound Gaussian distribution with the inverse Gamma texture is adopted to model the sea clutter. Moreover, the mean of sea clutter signals is assumed to be nonzero and unknown. The novel adaptive detector based on the two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is proposed. Firstly, we derive the test statistic of the proposed detector under the condition that the inverse Gamma texture, the mean vector (MV), and the covariance matrix (CM) are assumed to be known. Secondly, the adaptive detector can be obtained by replacing them with their estimates. Simulation experiments are conducted using the synthetic nonzero-mean compound Gaussian sea clutter data. The numerical results indicate the performance of the proposed detector.