Adaptive Detection of Spread Targets in Nonhomogeneous Environments: A Bayesian Approach

The problem of adaptive detection of spatially distributed targets or targets embedded in no homogeneous clutter with unknown covariance matrix is studied. At first, assume the clutter is complex circular zero-mean Gaussian clutter with an unknown positive definite covariance matrix, and it is independent of the covariance matrix vector under test, the secondary data are assumed to be random, then the properties of complex Wish art distributed is researched. Next, the Generalized Likelihood Ratio Test (GLRT) decision statistic based on Bayesian methods is derived, and then the numerical results are presented by means of Monte Carlo simulation strategy. Assume that cells of signal components are available, in this context, the simulation results highlight that the influence of different numbers of secondary data on detection performance, finally the influence of dispersion exponent on detection performance is studied.