Adaptive radar detection of distributed targets in non-Gaussian noise

This paper deals with the problem of detecting distributed targets in non-Gaussian noise with unknown statistics. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels. We also assume that a set of secondary data, free of signal components, is available to estimate the covariance matrix of the disturbance. Since no uniformly most powerful (UMP) test exists for the problem at hand we devise and assess two detection strategies based on the Rao test and the Wald test respectively. Remarkably these detectors ensure the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix as well as the power levels. Moreover, the performance assessment, conducted also in comparison with the generalized likelihood ratio test (GLRT) based receiver proposed in Conte et al. (2002), shows that the Wald test outperforms the others and is very effective in scenarios of practical interest for radar systems.