Adaptive sparse range-spread target detection in homogeneous generalized Pareto clutter

The detection problem of the sparse range-spread target against correlated non-Gaussian clutter is considered in this paper, and the clutter is modeled as the compound-Gaussian (CG) distribution. The CG clutter's texture is modeled as the square root of an inverse-gamma distribution, and the clutter's power obeys the generalized Pareto (GP) distribution. The optimum range-spread target in homogenous GP power-clutter is devised. By substituted the target's ideal amplitude with its maximum likelihood estimate (MLE) and replacing the ideal clutter covariance matrix with its estimate from the secondary data, the generalized likelihood ratio test is then obtained. The performance assessment is conducted by Monte Carlo simulation, and presents the effectiveness of our proposed detector.